High-Concurrency Locking in R-Trees
Marcel Kornacker
Universit¨a t Hamburg
22527Hamburg,Germany kornacke@https://www.doczj.com/doc/8c8623308.html,rmatik.uni-hamburg.de
Douglas Banks University of California at Berkeley Berkeley,CA94720-1776,U.S.A dbanks@https://www.doczj.com/doc/8c8623308.html,
Abstract
In this paper we present a solution to the problem of concurrent op-erations in the R-tree,a dynamic access structure capable of storing multidimensional and spatial data.We describe the R-link tree,a variant of the R-tree that adds sibling pointers to nodes,a technique ?rst deployed in B-link trees,to compensatefor concurrent structure modi?cations.The main obstacle to the use of sibling pointers is the lack of linear ordering among the keys in an R-tree;we overcome this by assigning sequence numbers to nodes that let us reconstruct the“lineage”of a node at any point in time.The search,insert and delete algorithms for R-link trees are designed to completely avoid holding locks during I/O operations and to allow concurrent modi?-cations of the tree structure.In addition,we further describe how to achieve degree3consistency with an inexpensive predicate locking mechanism and demonstrate how to make R-link trees recoverable in a write-ahead logging environment.Experiments verify the per-formance advantage of R-link trees over simpler locking protocols. 1Introduction
One of the future requirements for databases is the ability to support multidimensional and spatial data.This support is crucial for non-traditional database applications such as CAD,Geographical Information Systems(GIS)or temporal databases,to name a few.A fundamental aspect of support for spatial data is ef?cient handling of range queries along multiple dimensions;one example is the retrieval of points that intersect a given query rectangle.The most widespread This work was done while the author was visiting the University of California,Berkeley.It was supported by the Defense Advanced Re-search Projects Agency under grant T63-92-C-0007and the Army Research Of?ce under grant91-G-1083.The author’s new address:University of California at Berkeley,Berkeley,CA94720-1776,U.S.A.;e-mail:mar-cel@https://www.doczj.com/doc/8c8623308.html,.
Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage,the VLDB copyright notice and the title of the publication and its date appear,and notice is given that copying is by permission of the V ery Large Data Base Endowment.To copy otherwise,or to republish,requires a fee and/or special permission from the Endowment.
Proceedings of the21st VLDB Conference
Z¨urich,Switzerland1995access method,the B-tree[BaMc72],does not handle multi-dimensional data very well.
[Gutt84]proposed a spatial access method designed to handle multidimensional point and spatial data.Unlike other spatial access methods[Bent75,Niev84,Robi81,LoSa90], R-trees are not restricted to storing multidimensional points, but can directly store multidimensional spatial objects,which are represented by their minimal bounding box.R-trees have not bene?ted greatly from the many re?nements and opti-mizations of concurrency mechanisms that have been de-signed for B-trees.A particular modi?cation of B-trees,the B-link tree[LeYa81],connects the siblings on each level via rightward-pointing links and compensates for un?nished splits by moving across these links.This technique avoids holding locks during I/O operations and has recently been shown to offer the highest degree of concurrency among locking protocols for B-trees[SrCa91,JoSh93].Unfortu-nately,the B-link tree technique expects the underlying key space to have a linear order and therefore cannot be directly applied to R-trees.
In this paper we present R-link trees[BKS94],an exten-sion of R-trees motivated by Lehman and Yao’s work that shows similiar locking behavior and therefore offers the same high degree of concurrency as B-link trees.We circumvent the requirement for linearly ordered keys by introducing a system of sequence numbers that are assigned to each page and are used to determine when and how to traverse sibling links.Our deletion algorithm removes nodes as soon as they become empty without the need for a separate reorganization phase,a novel feature for link-style trees.
The remainder of this paper is organized as follows.Sec-tion2provides background on R-trees and B-link trees.Sec-tion3goes into detail on the dif?culties in applying the struc-tural modi?cation of B-link trees to R-trees,presents the for-mal de?nition of an R-link tree and describes the search and insert algorithms.It also sketches the deletion algorithm. Section4shows how to make scan results serializable.Next, section5presents a way to make R-link trees recoverable in a write-ahead logging environment.Section6presents perfor-mance results and section7provides a discussion of related
work.Finally,section8gives a brief summary.
2Background and Motivation
2.1R-Trees
An R-tree is a hierarchical,height-balanced indexing struc-ture similar to a B-tree.Like B-trees,R-trees have leaf nodes and internal nodes with entries in leaf node pointing to disk records and entries in internal nodes pointing to other internal nodes or leaf nodes.A node corresponds to a disk page and has between and entries().The only ex-ception is the root,which may hold between and entries. Unlike in B-trees,the keys in R-trees are multi-dimensional objects that have no linear order de?ned on them.
An entry in a leaf node of an R-tree contains a disk tu-ple identi?er and the key,which is either a multidimensional point or a rectangular outline of the spatial object it repre-sents.An entry in an internal node summarizes the node it points to by storing as the key the minimum bounding rect-angle that tightly encloses all the keys in the child node. The information contained in an R-tree is thus hierarchi-cally organized and every level in the tree provides more detail than its ancestor level.A pointer to an indexed ob-ject is stored in the tree only once,but keys at all levels are allowed to overlap,possibly making it necessary even for point queries to descend multiple subtrees.Since multidi-mensional keys cannot be linearly ordered there is no single “correct”place for a particular key;consequently,it can con-ceivably be stored on any leaf.
The search process in an R-tree is very different from that in a B-tree due to the lack of ordering and the possible overlap among keys.For example,to?nd all rectangles intersecting a given range the search process has to descend all subtrees that intersect or fully contain the range speci?cation.Further-more,since an entry in an internal node summarizes the child node with a bounding rectangle,there is no guarantee that the child contains any keys of interest,even if its bounding rect-angle intersects the search range.
The strategy for placing entries on leaf nodes should there-fore create an ef?cient index structure that optimizes retrieval performance.The literature has identi?ed a variety of pa-rameters for the layout of keys on nodes that affect retrieval performance[BKSS90,SRF87].These parameters are:min-imal node area,minimal overlap between nodes,minimal node margins or maximized node utilization.It is impossible to optimize all of these parameters simultaneously.For in-stance,the original R-tree proposal[Gutt84]minimizes over-lap between nodes;the R*-tree variation[BKSS90]mini-mizes overlap for internal nodes and minimizes the covered area for leaf nodes.
When a new key has to be inserted in an R-tree,we attempt to descend to the geometrically optimal leaf by picking at each level the subtree with the optimal bounding rectangle. In contrast to B-trees,R-trees have to recursively update the ancestor keys if a leaf’s bounding rectangle changes. Splitting a node also deviates noticeably from the B-tree pattern.Whereas the B-tree simply“cuts”the sequence of keys stored in the over?owing node in half,the R-tree will partition the key sequence according to its layout strategy. Figure1illustrates the scenario of a split where the layout strategy is minimal overlap.Note in this example that it is impossible to completely avoid any overlap.
2.2Concurrency in B-Trees
When multiple search and insertion processes are carried out on a B-Tree in parallel,their interactions may be interleaved in a way that leads to incorrect results.Simple solutions to this problem have the insertion process lock the entire tree or the subtree that needs to be modi?ed due to anticipated splits.V ariations thereof lock the upper levels of the subtree so that only readers can still access it[BaSc77].In essence all of these methods employ top-down lock-coupling:when descending the tree a lock on a parent node can only be re-leased after the lock on the child node is granted.When doing lock-coupling,locks are held during I/O operations,which should be particularly detrimental to high concurrency of in-sert and delete operations in R-trees.When descending the tree via lock-coupling,locks can be acquired in shared mode, allowing many search and update operations to descend the tree concurrently.But update operations can block on cou-pled read locks during tree ascent.For B-trees,tree ascent only takes place as a result of a node split or deletion.For R-trees,it also takes place in order to propagate a changed bounding rectangle.The latter can be expected to occur far more frequently and unpredictably than node splits or dele-tions.
A radically different approach was proposed in[LeYa81]. Instead of avoiding possible con?icts by lock-coupling,the tree structure is modi?ed so that the search process has the opportunity to compensate for a missed split.The crucial addition is the rightlink,a pointer going from every node to its right sibling on the same level(excluding the rightmost nodes).When a node is split and a new right sibling is cre-ated,it is inserted into the rightlink chain directly to the right of the old one.The effect is that all nodes at the same level are chained together through the rightlinks.Furthermore,the sequence of the nodes in the rightlink chain re?ects the se-quence of their corresponding entries in the ancestor level; in short,the rightlink chain orders the nodes by their keys. This is true for every level of the B-Tree and is a result of the splitting strategy in B-Trees,where the upper half of the key sequence is moved to the new right sibling.
Searching in a B-link tree can therefore be done without lock-coupling.When descending to a node that was split after examining the parent,the search process discovers that the highest key on that node is lower than the key it is looking for and correctly concludes that a split must have taken place. It compensates for this split,or multiple splits,by moving
3578
6
2
4
8
1
6724+
1
35Figure 1:Overlap can be unavoidable after a split.
right until it comes to a node where the highest key exceeds the search key.Likewise,an insertion process does not have to employ lock-coupling when descending the tree to the correct leaf.If the leaf has to be split,it is also possible to avoid lock-coupling when installing a new entry in the parent,as is shown in [LaSh86]and [Sagi86].As soon as the page has been split and the new right sibling inserted into the rightlink chain,the insertion process can drop the lock on the leaf that was over?owing and then acquire a lock on the parent,possibly moving right to compensate for concurrent splits and possibly splitting the parent itself,leading to recursive splits up the tree.This locking strategy is deadlock-free and offers very high concurrency because search and insertion processes only need to hold one node locked at a time.
3R-Link Trees
We would like to achieve high concurrency for operations on R-trees,and given the similarities in structure and function-ality between B-trees and R-trees,it would seem natural to try to apply the ideas and algorithms of [LeYa81]to create an “R-link tree.”This is not a trivial matter,however,because R-trees differ from B-trees on a number of important points and the B-link tree strategy itself is insuf?cient.
The source of this problem is the lack of ordering on R-tree keys.The core of the link-tree strategy is to account for splits that have not updated the parent by moving to the right.To implement that strategy we must answer two questions:how do we detect that the child has split and how do we limit the extent to which we move right.For R-trees,the latter question is not only relevant for ef?ciency,it is relevant because we descend multiple subtrees and may therefore end up visitingthe same node twice if we move too far to the right.For B-link trees,the answer to those questions lies in the linear ordering that is de?ned on the key space and the fact that the nodes on a single level are ordered through the rightlink chain by their keys.This allows us to detect a split and to determine when to stop moving right based on key comparisons.It is impossible to apply the same strategy to R-trees.First of all,keys cannot conclusively tell us when a node has split.It is possible that the key of an entry in the parent intersects the search range,even if the keys in the child do not.In this case,it would be wrong to conclude that the child has split and move https://www.doczj.com/doc/8c8623308.html,ing a notion analogous
to the high key in a B-tree,we could also recompute the bounding rectangle of the child node and compare that to the key seen in the parent in order to detect a split.Doing so might cause us to miss a split because taking entries out of a node does not necessarily change its bounding rectangle (see ?gure 1).But even if we are sure that a node has split,it is impossible to limit the extent to which we move right by doing key comparisons.Adjacent nodes in the rightlink chain might have a bounding rectangle that intersects our search range,even though they did not take part in the split we detected.As mentioned before,we must not visit these nodes via rightlink traversal because we will visit them later on while searching a different path in the tree.
We need to provide each operation on an R-tree with a way of determining whether it has accurate information about the current state of any node it might examine,and how it should proceed if it ?nds that its information is obsolete.3.1Structure of an R-Link Tree
Clearly,if we are to provide high concurrency operations on R-trees through a rightlink-style approach,we need to add some additional information to the standard R-tree that can be used to correctly traverse a constantly-changing tree struc-ture.We propose ful?lling this requirement by assigning log-ical sequence numbers (LSNs)to each node.These numbers are similar to timestamps in that they monotonically increase over time but are not synchronous with any real-time clock.The node entries and the search and insert algorithms are de-signed so that these LSNs can be used to make correct deci-sions about how to move through the tree.
An R-link tree is basically a standard R-tree,as described in section 2.1,with two key differences.First,like a B-link tree,all of the nodes on any given level are chained together in a singly-linked list via rightlinks.It is very important to note that,unlike the B-link tree,the chain of nodes on a given level does not represent an ordering of the keys from smallest to greatest,and,in general,it will not re?ect the ordering of their corresponding entries in the nodes on the parent level.This is illustrated in ?gure 2.In the rightlink chain of the parent level,precedes .However,,which is a child of ,does not precede .This situation can arise if splits and moves the entry for over to the new right sibling,.Second,the main structural addition is an LSN in each node that is unique within the tree.These LSNs give us a
5
6
4
2
1
5x 2
y 4c5
w 1
z p1
p2
c2
c1
c3
c4
Figure 2:A subsection of an R-link tree (circled numbers are LSNs).
mechanism for determining when an operation’s understand-ing of a given node is obsolete.Each entry in a node con-sists of a key rectangle,a pointer to the child node and the LSN that it expects the child node to have.If a node has to be split,the new right sibling is assigned the old node’s LSN and the old node receives a new LSN.A process traversing the tree can detect the split even if it has not been installed in the parent by comparing the expected LSN,as taken from the entry in the parent node,with the actual LSN.If the lat-ter is higher than the former,there was a split and the process moves right.When the process ?nally meets a node with the expected LSN,it knows that this is the rightmost node split off the old node.
R-link trees can be formally de?ned as a balanced tree in which index nodes consist of a set of entries and a rightlink .On each level of the tree the rightlinks form the nodes on that level into a singly-linked list.Entries on internal nodes consist of a key rectangle ,a pointer ,and an expected LSN so that either:
1.(normal case –child-level structure fully re?ected in par-ent)
points to a child node ,where is the LSN of ,and the rightlink of points to NULL or to some node which is also pointed to by some entry in the level above.In ?gure 2,entry points to node ;both ’s LSN and ’s LSN are matching and ’s rightlink points to ,which is also pointed to by entry in .
2.(uninstalled split in child level compensated by rightlink)
points to a child node ,where the LSN of is greater than ,and there exists a node whose LSN is ,which can be reached by following rightlinks from through nodes with LSNs higher than which are not pointed to by any entry in the level above.also has no entry in the level above,but its right sibling,if is not the end of the chain,does.An example from ?gure 2is the entry in .The LSN in is smaller than that of and equal to the LSN of ,which in turn can be reached from by following one rightlink.Node does not yet have an entry in the level above,but its right sibling,node ,is
pointed to by entry in .This situation was caused by a split of node ,which has not yet been installed in the parent node.
Note that in either case,the right sibling R of the node whose LSN matches the entry’s expected LSN has an entry in some node on the parent level.This entry can generally be anywhere in the parent level.Node in ?gure 2is an example where this entry is in a node to the left of the parent node of .
3.2The Search Algorithm
A search process has to ?nd all the entries on leaf nodes that fall in the query range,and since keys can overlap,it will generally have to descend multiple subtrees within the index.The underlying data structure to support this is a stack,which is used to remember which nodes still have to be visited.The process starts by initially pushing the root on the stack.A node that has not yet been examined is popped off the stack and all entries in the node that qualify for the search condition are in turn pushed onto the stack and the whole process is repeated.If a leaf node is popped off the stack,we can return the qualifying entries that we ?nd on it.The search is terminated when the stack is empty.
In order to remember a yet-to-be visited node on the stack,we push the pointer and the LSN we found in the correspond-ing entry.If we examine a node and ?nd that the LSN is higher than the one on the stack,we know that this node has been split in the meantime.To compensate for the split we must examine all of the nodes that have been split off from this node since we ?rst pushed its entry.Therefore we push nodes to the right of ,up to and including the node with the LSN equal to the expected LSN for .
The search process,as shown in ?gure 3is implemented with an iterator-like interface.The ?rst call to search will return the ?rst record and subsequent calls to continueSearch will return all other matching items until the stack is empty.
3.3The Insertion Algorithm
An insertion proceeds in two stages:?rst we must locate the leaf to insert the key on,remembering the path we take as
search(Rect r):
push(stack,[root,root-lsn])
return reduceStack(r)
continueSearch(Rect r):
return reduceStack(r)
reduceStack(Rect r):
while not empty(stack)
[p,p-lsn]=pop(stack)
if(p is pointer to indexed tuple)
return p
else
r-lock(p)
if p-lsn LSN(p)
traverse the rightlink chain
starting at rightlink(p)
to the node with
LSN=p-lsn;
for each node n along the
rightlink chain:
r-lock(n)
push(stack,[n,LSN(n)])
r-unlock(n)
end
for all entries e of p
intersecting r:
push(stack,
[node-pointer(e),LSN(e)])
r-unlock(p)
end
end
Figure3:The search algorithm
we descend the tree;next,then the new key is inserted and the leaf possibly split.If the leaf’s bounding rectangle has changed,we must propagate the change to its ancestor node. This is accomplished by backing up the tree until we arrive at a parent node that does not need to be changed.If the leaf was split we must also install a new entry in the parent node. If it is full,we recursively split nodes up the tree until we arrive at a node with enough free space or alternatively split the root.Note that in contrast to a B-tree insertion,we must back up the tree for two reasons:splitting a node requires the installation of a new entry and changing the bounding rectangle requires the adjustment of the keys in the ancestor nodes.1The latter step is missing in B-trees.
When descending the tree to a leaf,we choose the geomet-rically optimal subtree at each level.However,if we detect that a node has been split,we must take into consideration all the nodes to the right of the original node that were split off it. As in the search algorithm,this chain is delimited to the right by the node carrying the original LSN.When we are updat-1These two changes have to be applied atomically in order to guarantee the R-link tree properties of section3.1.Atomic changes are further dealt with in section5.ing parent keys during ascent,we also must move right if the parent node has split.Notice in this case that no LSN is nec-essary to recognize the split or delimit the rightlink chain.An entry in a node can be uniquely identi?ed by the node pointer2 it contains;for that reason,we move right until we?nd the node with that particular entry.
When backing up the tree one level,we employ lock-coupling;that is,we hold the child node write-locked until we obtain a write-lock on the parent.If we do not couple the locks,another inserter causing a split can overtake us and install the changes before us.When it is?nally our turn, we would update the key,unaware of the previous changes to the child node.The key would not re?ect the bounding rectangle of the child anymore and the tree structure would be incorrect.3This is a deviation from the locking behavior in B-link trees,where lock-coupling during ascent is not necessary.In practice,this should make little difference to the achievable degree of concurrency,because parent nodes can be expected to still reside in main memory and therefore no locks are held during I/O operations.
The implementation of the insert algorithm is shown in?g-ure4.The individual procedures do the following:?ndLeaf descends to the geometrically optimal leaf,recording the path along the way and?nally write-locks the leaf;extendParent is called after a leaf split to recursively install an entry for the new leaf in the parent and to propagate the changed bounding rectangle of the old leaf;updateParent is called only after a leaf’s bounding rectangle has changed in order to recursively propagate the new bounding rectangle of that leaf.
To keep the algorithm as short as possible,we do not consider the case where multiple insertions are carried out at the same time and a splitting of the root by one inserter goes unnoticed by the others.This is problematic when the remaining inserters have to change the bounding rectangle of what they believe is the root or if the“root”has to be split a second time.A solution can be found in[LaSh86] and[Sagi86];both suggest using an anchor page to make root splits visible to other insertion processes and allow for compensating actions.
There is one restriction to consider when installing a new child entry in a parent node,which might not be immediately obvious.If the parent node was split,we would like to insert the new entry on the geometrically optimal node in the chain. Unfortunately,this is not possible and the new entry has to be installed on the same page that contained the old entry(or its new right sibling if the insertion causes a split).The reason for this can be seen in?gure5.Suppose a search process is looking for item contained in leaf node(situation a).
2Node pointers do not change after a split because the original node is kept in place.
3It is not necessary to do lock-coupling when moving right.If another inserter overtakes us while we are moving right and splits the nodes we are examining,it is impossible for us to miss the entry for the child node since a split can move entries only right.
insert(Rect r):
stack=?ndLeaf(root,r,root-lsn)
leaf=pop(stack)
insert r on leaf
if leaf was split
extendParent(leaf,
bounding-rect(leaf),
LSN(leaf),right sibling,
bounding-rect(right sibling),
LSN(right sibling),stack)
else
if bounding-rect of leaf changed
updateParent(leaf,
bounding-rect(leaf),stack)
else
w-unlock(leaf)
end
end
Stack?ndLeaf(RTreeNode p,Rect r,LSN p-lsn): if p is leaf
w-lock(p)
else
r-lock(p)
end
if p-lsn LSN(p)
p=geometrically optimal node to take
r in rightlink chain starting at p
and ending at node with
LSN=p-lsn
end
if p is leaf
push(stack,p)
return stack
else
e=entry on p leading to
geometrically optimal subtree
for r
push(stack,p)
r-unlock(p)
return?ndLeaf(e,r,LSN(e))
end
extendParent(RTreeNode p,Rect p-rect,
LSN p-lsn,RTreeNode q,Rect q-rect,
LSN q-lsn,Stack stack):
if empty(stack)
create new root(w-locked)with2
entries:
-for child,key:p-rect
-for sibling,key:q-rect
w-unlock(q)
w-unlock(p)
w-unlock(new root)
return
else
parent=pop(stack)
w-lock(parent)
?nd the entry for node p in parent
or one of its right siblings;
let parent=that node and
entry=that entry
w-unlock(q)
w-unlock(p)
update entry with p-rect and p-lsn
insert q on parent
if parent split
extendParent(parent,
bounding-rect(parent),
LSN(parent),
right sibling,
boundingRect(right sibling),
LSN(right sibling),stack)
else
if bounding-rect(parent)changed
updateParent(parent,
bounding-rect(parent),
stack)
else
w-unlock(parent)
end
end
end
updateParent(RTreeNode p,Rect p-rect,Stack stack): if empty(stack)
w-unlock(p)
return
else
parent=pop(stack)
w-lock(parent)
?nd the entry for node p in parent or
one of its right siblings;let
parent=that node and
entry=that entry
w-unlock(p)
update key in entry with p-rect
if bounding-rect(parent)changed
updateParent(parent,
bounding-rect(parent),stack))
else
w-unlock(parent)
end
end
Figure4:The insertion algorithm
Node and are split independently and item is moved to the new leaf(situation b).If the splitting of is already re?ected in the parent level,the search process will navigate directly to.In situation c,the entry for has been installed in,because this results in a geometrically better node layout than a placement on,and the entry in for has also been
x x 77
8
77x 778
8c’
(a)
(b)
(c)
f c f c f’c’f c f’
Figure 5:An incorrect structure modi?cation.
updated (key and LSN).In this case,the search process will be unable to ?nd leaf because it never considers going to .On the other hand,if the entry for had been installed in ,the search would have been successful.
In principle,this requirement could deteriorate the tree structure by forcing its keys to have more overlap than nec-essary.We expect this potential drawback to have little effect in practice because only in rare cases will the geometrically optimal node differ from the node containing the entry for the old child.
3.4Deletion
A key deletion from a leaf node is a combination of the search and insert algorithms.First,we have to ?nd the leaf holding the key we are interested in.After the key is removed from the leaf,we have to propagate the changed bounding rectangle up the tree,in the same manner as updateParent does for an insertion.The locking behavior is the same as for the search and insert algorithms and therefore key deletion is also deadlock-free.
If we want to maintain a high storage utilization,we have to remove nodes as soon as they become empty,so that they can be reused for subsequent splits in the tree.Since we do not employ lock-coupling when descending the tree,we have to deal with the problem of invalid pointers when removing nodes.In order to let descending operations detect that they followed an invalid pointer and reached a deleted node,we introduce a tree-global generation counter,which is a logical timestamp similar to an LSN.Each node in the tree stores its generation value in its header.On node removal,the global counter is incremented and the new value assigned to the deleted node,so that removed nodes have chronologically increasing generation values.A descending operation can now detect an invalid pointer by remembering the value of the global generation counter when reading the pointer and comparing this value to the one stored in the node header when ?nally visiting the node.A higher counter in the node indicates that it must have been removed after the pointer was read.In this case,a descending operation has to be restarted from the lowest valid ancestor node.The same compensating measure is necessary when the operation follows an invalid rightlink.This is detected if the generation value of the right sibling is higher than the global counter at the time the
pointer to the original child node was read (at the time the parent,not the original child,was examined).The R-link tree structure as postulated in section 3.1has to be slightly relaxed to accomodate the case where a node pointer is invalid.The removal of the node and its corresponding entry in the parent are carried out recursively,also removing the parent if it becomes empty and so forth.Note that it is impossible for us to cheaply access the left sibling of the deleted node in order to reset its rightlink.But because a rightlink in the R-link tree is only needed to compensate for splits as long as these are not fully re?ected in the parent level,we need not ?x the rightlink chain when removing a node.An operation that crosses an invalid rightlink detects this and restarts itself;all subsequent operations will see a parent that fully re?ects the child level and will not have to traverse the rightlink anymore.We can therefore safely ignore the invalid rightlink and as a consequence do not have to access the left sibling at all.Contrast this with B-link trees,where the rightlink chain at the leaf level is also used by range scans and a node deletion would therefore have to reset the rightlinksof neighboring nodes.
During the entire node deletion process,we only have to keep two nodes locked at a time:the node to be deleted and its parent node.
4Consistency
A common requirement for concurrent access in database systems is degree 3consistency ,or repeatable read (RR)[Gray78].A simple solution employed for B-trees is to keep all leaf pages that were read by an index scan locked until the end of the transaction.This strategy depends on the linear order of the keys and leaves and the fact that index scans always visit a contiguous sequence of leaves.
In R-trees,keys can be inserted on arbitrary leaves and an insertion into the key range of a previous scan can succeed even though the scan locked all of the leaves it read.If the insertion commits,the new key will be visible to a re-scan,giving rise to a phantom.An example for a two-dimensional key space is shown in ?gure 6.Boxes and are the bounding rectangles of internal nodes,boxes to are the bounding rectangles of leaves and the dashed box is the query rectangle.If the scan is looking for overlapping keys,only leaf quali?es and consequently it is the only leaf that is
2
3
4
5
61
Figure 6:An example where 2-phase locking of leaves can-not guarantee RR.
visited and locked.The insertion of a new key into leaf extends its bounding box into the query rectangle,so that a re-scan will be able to see the new key,violating degree 3consistency.
One way to avoid the phantom problem is for scans to keep every node they traversed locked until the end of the trans-action,including internal nodes.This way,even a success-ful insertion into a leaf cannot propagate the new key so far up the tree that a scan with a con?icting key range can see it.The major disadvantages are that by setting locks on in-ternal nodes it reduces concurrency more than necessary and also introduces deadlocks.A searcher descending the tree can now collide with an inserter propagating changes up the tree.
A more effective solution to the phantom problem is to use a simpli?ed form of predicate locks [EGLT76],where exclu-sive predicates consist of a single key value and shared pred-icates consist of a query rectangle and scan operation such as inclusion or overlap.A new scan request would check the still-active insertions and suspend itself if its query rectangle collides with any of the uncommitted new keys.A new in-sertion would in turn check the active scans and also suspend itself on a collision with a query rectangle.If a scan commits and leaves the system,the waiting inserters are rechecked to see if some can be activated;the case of an inserter com-mitting is handled symmetrically.The advantages of this over the former page-locking scheme are that no deadlocks are possible and concurrency is not unnecessarily restricted,since an insertion can still propagate changes up to the root as long as it does not fall in the speci?ed ranges of active scans.The disadvantages attributed to general-purpose pred-icate locks for tables,exponential runtime and overly pes-simistic behaviour,do not apply here.To evaluate a predicate we simply check a key value against a query rectangle and a lock request is only rejected if there is a guaranteed collision with another active lock.Key values and query rectangles can be organized into an in-memory spatial data structure to speed up checking for lock con?icts.
5Recovery
The main ideas of the recovery method we present for R-link trees are drawn from [MoLe89]and [LoSa92].Like these two papers,we also split an update operation into its contents-changing and its structure-modifying part.A con-tents change is an insert or delete of a key on a leaf;a struc-ture modi?cation can be a node split or deletion,an up-date of an index entry on an inner node or the insertion or deletion of an index entry on an inner node.By separat-ing these two,the semantic effects of an index operation are attributed to the initiating transaction whereas the struc-ture modi?cation is handled independently of any transac-tion.This is necessary so that structure modi?cations can be made visible immediately after their completion and do not have to be locked until the initiating transaction commits.Like [MoLe89]and [LoSa92],we assume that write-ahead logging (W AL)is used for recovery purposes.In addition,we also assume that logical undo is supported.This is a ne-cessity for the R-link tree,which is explained shortly.
When changing the contents of a leaf through an insert or delete of a single key,we log this operation within the context of the executing transaction,obeying the W AL protocol.This ensures that these leaf changes can always be undone if the transaction aborts and redone if it commits.Structural mod-i?cations,on the other hand,are treated as separate recover-able units (“atomic actions”in [LoSa92]and “nested top ac-tions”in [MoLe89]).For example,a node split is an atomic action,which is caused by a transaction trying to insert a key on a full leaf node.The atomic action is logged and recov-ered separately from the surrounding transaction.Also,the atomic action is “committed”as soon as it ?nishes 4and not rolled back even if the transaction that caused it fails.
The obvious advantage is that the effects of structure mod-i?cations can be made visible to other transactions although the initiating transation has not committed,without creat-ing an abort dependency between the transactions.In other words,we do not have to keep the pages involved in a split locked until the end of transaction.A prerequisite of this strict separation is the ability to support logical undo as op-posed to a purely page-oriented undo.As an example,con-sider the case where a transaction inserts a key on a page and the page is subsequently split,with the new key moved to the right sibling.If later on the transaction aborts,the inserted key cannot be found on its original leaf and the tree has to be traversed,undoing the insert with a delete operation.Logical undo is important in R-link trees for another reason,which the following example illustrates.When deleting a key,we also adjust the ancestors’bounding rectangles to re?ect the missing key.Like all structure modi?cations,these updates are committed immediately.When rolling back the delete op-4Note
that the atomic action’s log records need not be ?ushed when the
atomic action ?nishes.It is suf?cient that they be ?ushed with the log records of the next committing transaction,because no previously committed trans-action could possibly have seen the effects of the atomic action.
eration,it is seldomly suf?cient to do a page-oriented undo and just re-insert the key on the leaf page.We might also have to adjust the bounding rectangles and therefore perform a complete insertion operation.
Like[LoSa92],we break up entire structure modi?cations such as node split or delete propagation into several atomic actions that operate on at most two levels of the tree and are executed serially.These atomic actions are:
splitting a leaf node
changing an existing entry in an individual invocation of updateParent
changing an existing entry and adding a new entry in an individual invocation of extendParent(this might include
a split)
incrementing the global generation counter by1and re-moving a node and its entry in the parent(the node is marked as“removed”by assigning it the new generation counter value)
The boundaries of atomic actions are chosen to coincide with the visibilityboundaries induced by the locking protocol.Put differently,an atomic action“commits”before it releases the locks it holds.Only for node removal does an atomic action span more than a single level of the tree.The reason is that we might otherwise be left with an invalid pointer after having incremented the generation counter and marked the node as deleted.If the system crashed before removing the entry from the parent,a descending operation would later on not be able to detect that the pointer is invalid.
Since the structural modi?cations are carried out level by level,it is possible that they are interrupted by a system crash, possibly leaving behind index entries in inner nodes with “wide”bounding rectangles or nodes without a correspond-ing parent entry.This does not render the tree inconsistent, but it deteriorates the structure because subsequent search operations might be misled into unnecessarily following a pointer or forced to do rightlink traversals for lack of a par-ent entry.We can repair these structural de?ciencies on the ?y.Wide bounding rectangles are implicitly repaired by a subsequent insert or delete operation that adjusts bounding rectangles along the same path.When an ascending structure modi?cation notices that a node on its path,which it reached by following a rightlink,does still not have a parent entry, it has detected an un?nished split.This has to be explicitly repaired by supplying the missing entry to the parent.When doing this,one has to be careful about obeying the required locking order and possibly release and reacquire some locks already held.
When combining this recovery mechanism with our pro-posed predicate locks,a rolling-back transaction is guaran-teed never to be involved in a deadlock.This characteris-tic stems from a)the deadlock-free locking protocol inside the tree and b)the fact that predicate locks can be fully ac-quired before the tree operation is started and any node locks are acquired.The consequence of the latter is that there cannot be a deadlock between a predicate lock and a node lock.Because of this freedom from deadlock we can also use cheap latches[MoLe89]instead of locks when locking the tree nodes for physical consistency.
6Performance Measurements
To asses the performance of R-link trees relative to non-link style R-tree concurrency mechanisms,we implemented R-link trees as a new access method for the Illustra database engine[Illu94]and compared it to the existing R-tree implementation.The performance numbers were obtained from concurrent client processes accessing a fully-functional database system,as opposed to simulation results or calls to a stand-alone access method implementation.
The existing R-tree implementation employs a variation of Bayer-Schkolnick-style lock-coupling for concurrent inser-tions.An insert operation obtains update locks on all nodes from the root to the leaf,holding on to them until the oper-ation is?nished.An update lock is incompatible with write locks and other update locks.This prevents two insert oper-ations from getting a lock on the same node,thereby allow-ing a lock holder to escalate the update lock to a write lock without running the risk of a deadlock.This locking strategy allows multiple readers to be active in an index but serializes insertions,because the update lock on the root can only be help by one transaction at a time.Both R-link and R-trees employed the same page splitting strategy,a simpli?ed ver-sion of Guttman’s original quadratic split algorithm.We did not implement all of the recovery strategy outlined in sec-tion5,because the no-overwrite storage manager[Ston87] of the Illustra database server does not undo the changes of aborted transactions.Also the deletion algorithm was not im-plemented as described in section3.4,because index reorga-nization is performed off-line.
We investigated the throughput and response time of search and insert operations on the index.Analogous to[SrCa91],we de?ned three different workloads:the low-contention workload consists of a varying number of searchers and a single inserter,the moderate-contention workload of inserters only and the high-contention workload of an equal number of searchers and inserters.The effect of multiple users is obtained by concurrently running client processes which continuously send requests to a database server.The Illustra database engine has a process-per-user architecture,so that every process had its own server. Both insert and search operations were executed as SQL statements inserting or retrieving a single rectangle.The statements were executed within the context of a transaction, requiring the writing of a log record.
Prior to each run at a particular multiprogramming level,
Figure7:Total Throughput
the table was preloaded with30,6002-dimensional,non-overlapping rectangles,each measuring10x10.Together,the rectangles imposed a grid pattern on the area1700x1800. Rectangles inserted during the benchmark measure8x8and were placed within a randomly chosen rectangle from the pre-loaded data set.The intention was to avoid propagating bounding box changes up the tree,so that we did not have to investigate the effects of locking for consistency.The search transactions also returned a single random rectangle from the pre-loaded data set.
The benchmarks were run on a dual-processor SPARCsta-tion10equipped with three DEC RZ28disks attached to a single controller.Both the table and the index used for the benchmark were striped across all3disks in a simple round-robin fashion.In order to compensate for the relatively small data set we limited the number of page buffers to64.The page size was8K,which resulted in a page capacity of70tu-ples for the relation,155index entries for R-link trees and 181index entries for R-trees.
The results we obtained for search transactions were es-sentially identical for both R-link and R-trees,under both the low-contention and the high-contention workload.This is not surprising,considering that neither R-link trees nor “Bayer-Schkolnick”R-trees lock out search operations when insertions are active in the tree.Also,the high-contention workload showed similar results for insert transactions as the medium-contention workload,only the numbers were scaled back due to the presence of resource-consuming searchers. For those reasons and space considerations,we decided to omit the results obtained from the low-and high-contention workloads altogether and focus on the all-inserters scenario. The effects of the different locking strategies on through-put and response time can be seen in?gure7and8, respectively.Both R-link and R-trees reach their maxi-mum throughput with three concurrent insertion processes. Whereas the R-link tree throughput is generally higher and
Figure8:Response Time
stays about constant,the R-tree throughput deteriorates considerably after that point.A likely explanation is that the process holding the root lock gets descheduled,thereby adding to the wait time of all of the blocked processes.The system then thrashes trying to?nd another process to run. The same effects can be seen in?gure8,where the response time for R-trees increases at a much higher rate than for R-link trees.
7Related Work
So far there has not been much work published on the con-currency control problem in R-trees.None of the algorithms known to us attempt to adapt the B-link tree strategy to R-trees in order to achieve higher concurrency.
Ng and Kameda[NgKa93]present three algorithms vary-ing in complexity and possible concurrency.The simplest of the three algorithms locks the entire tree so that an inser-tion would exclude all searchers.The second algorithm post-pones page splits by adding buffer space to each node to ac-commodate over?ows.When over?ows or under?ows take place,a separate maintenance process exclusively locks the entire tree and reorganizes it,splitting and merging several nodes in the same run.Because an insertion never performs a split itself,there is no need for concurrent search processes to do lock-coupling.The highest-concurrency algorithm is modeled after one presented in[BaSc77]for B-trees.Read-ers do top-down lock-coupling when descending the tree in order to avoid having to deal with splitting pages.Insertions lock the entire subtree that needs modi?cation on their way to the leaf.
[Moha89,MoLe89]and[LoSa92]discuss recoverable ac-cess structures in a write-ahead logging environment;the for-mer paper also present a solution for guaranteeing degree3 consistency with row-level locking.
ARIES/KVL and ARIES/IM both use a conventional,
non-link tree structure,yet they are able to propagate splits bottom-up without locking subtrees and to let top-down traversing processes recover from them.Instead of fol-lowing rightlinks,pages that are involved in a split are marked so that a search that runs into an ongoing split is able to notice it and retraverse the tree starting from the lowest unmodi?ed parent node.Unlike in a B-link tree,a partially executed structure modi?cation may leave parts of the tree temporarily invisible.Taking into account that some operations might require logical undo,which in turn requires tree traversal,it is necessary to serialize complete splits, including propagation,so that no two splits can take place at the same time.Moreover,there are situations in which insert or delete requests also have to be serialized with structure modi?cations.The tree must be consistent whenever logical undo could be necessary;this is achieved by waiting for ongoing structure modi?cations to?nish.To avoid the phantom problem when doing record-level locking,a scan also sets a shared lock on the next-highest key past its scan range.Again,this is not applicable in R-trees because the notion of a next-highest key does not exist.
The-tree presented in[LoSa90]is a generalization of a B-link tree where nodes can have multiple parents,which turns the tree structure into a DAG.The nodes of a-tree par-tition the key space,thus making it possible for descending operations to detect splits solely by key comparisons.Node consolidation requires lock-coupling during descent to avoid invalid pointers.As a consequence,lock-coupling cannot be employed during ascent without risking deadlocks.As men-tioned in section5,the-tree solution for recovery forms the basis of recovery in the R-link tree.The two approaches dif-fer in how un?nished splits are detected and repaired.A de-scending operation in a-tree takes a rightlink traversal as an indication of a possibly un?nished split and then checks the parent for the entry suspected missing.In an R-link tree, an un?nished split is unambiguously detected during ascent. 8Summary
In this paper,we have presented R-link trees,an extension on R-trees designed to support high concurrency.R-link trees look and work very similar to B-link trees,with each opera-tion holding only a few locks at one time and handling unex-pected splits by moving across link pointers to sibling nodes on the same level.The key differences in the design of B-link trees and R-link trees are a result of the fact that spatial keys cannot be ordered linearly.Where B-link trees rely on the actual keys involved in the search to resolve unexpected splits,R-link trees have to use a system of sequence numbers assigned to each node.The degree of concurrency obtainable with R-link trees should be as good as the best B-tree algo-rithm,the B-link tree.Descending an R-link tree to a leaf requires no lock-coupling;consequently,only a single node needs to be locked at any time.To cope with invalid point-ers resulting from node deletions,we keep track of when a node was deleted by assigning it a generation number.This allows us to do online node deletion without employing lock-coupling during tree descent.An insert or delete process as-cending the tree only needs to hold a maximum of two nodes locked,allowing many updates of the index to take place con-currently.For this to be possible,it is crucial that the recov-ery strategy support logical undo.A comparison with a lock-coupling concurrency mechanism for R-trees shows that R-link trees are capable of sustaining high throughput and low response times as the load increases.
An area for further research is the problem of enforcing de-gree3consistency of index scans.We outlined an inexpen-sive variant of predicate locking that we intend to investigate in more detail.The question remains whether popular tech-niques for B-trees such as key-range locking can be made to work for spatial data structures such as R-trees. Acknowledgement
We would like to thank Paul Aoki,Paul Brown,Bob Devine, Shel Finkelstein,Joe Hellerstein,Mike Olson,Sunita Sarawagi and Mike Stonebraker for their comments on this paper.We would also like to acknowledge Megan Metters who helped to make this paper more readable.The people at Illustra were also very helpful.In particular,we have to thank Jeff Meredith,Wei Hong and Mike Ubell for their support.Dan Seguin deserves special thanks for help with hardware emergencies.
References
[BaMc72]R.Bayer and E.McCreight,“Organization and Maintenance of Large Ordered Indexes,”Acta
Informatica,V ol.1,No.3,pp.173–189,1972. [BaSc77]R.Bayer and M.Schkolnick,“Concurrency of Operations on B-Trees,”Acta Inf.,V ol.9(1977),
pp.1–21.
[Bent75]J.L.Bentley,“Multidimensional Binary Search Trees Used for Associative Searching,”CACM,
September1975,V ol.18,No.9,pp.509–517. [BKS94] D.Banks,M.Kornacker,M.Stonebraker,“High-Concurrency Locking in R-Trees,”Sequoia2000
Technical Report94/56,June1994.
[BKSS90]N.Beckmann,H.-P.Kriegel,R.Schneider and B.
Seeger,“The R*-tree:An Ef?cient and Robust
Access Method for Points and Rectangles,”Proc.
1990ACM SIGMOD Conf.,pp.322–331. [EGLT76]K.Eswaren,J.Gray,R.Lorie and I.Traiger,“On the Notions of Consistency and Predicate Locks
in a Database System,”Comm.ACM,November
1976,V ol.19,No.11,pp.624–633.
[Gray78]J.Gray,“Notes on Data Base Operation Sys-tems,”Operating Systems,R.Bayer et al.(Eds.),
LNCS V olume60,Springer-V erlag,1978. [Gutt84] A.Guttman,“R-Trees:A Dynamic Index Struc-ture for Spatial Searching,”Proc.1984ACM SIG-
MOD Conf.,pp.47–57.
[Illu94]Illustra Information Technologies,Inc.,“Illustra User’s Guide,”Illustra Server Release2.1,June
1994.
[JoSh93]T.Johnson and D.Shasha,“The Performance of Current B-Tree Algorithms,”ACM TODS,V ol.
18,No.1,pp.51–101,March1993.
[LaSh86]https://www.doczj.com/doc/8c8623308.html,nin and D.Shasha,“A Symmetric Con-current B-Tree Algorithm,”1986Fall Joint
Computer Conference(Dallas,Tex.,Nov.1986),
pp.380–389.
[LeYa81]P.Lehman and S.Yao,“Ef?cient Locking for Concurrent Operations on B-Trees,”ACM TODS,
V ol6,No.4,December1981.
[LoSa90] D.Lomet and B.Salzberg,“The hB-Tree:A Mul-tiattribute Indexing Method with Good Guaran-
teed Performance,”ACM TODS,V ol15,No.4,
pp.625–685,December1990.
[LoSa92] D.Lomet and B.Salzberg,“Access Method Con-currency with Recovery,”Proc.1992ACM SIG-
MOD Conf.,pp.351–360.
[Moha89]C.Mohan,“ARIES/KVL:A Key-V alue Locking Method for Concurrency Control of Multiaction
Transactions Operating on B-Tree Indexes,”IBM
Research Report RJ7008,September1989. [MoLe89]C.Mohan and F.Levine,“ARIES/IM:An Ef?-cient and High Concurrency Index Management
Method Using Write-Ahead Logging,”IBM Re-
search Report RJ6846,August1989.
[NgKa93]V.Ng and T.Kameda,“Concurrent Accesses to R-Trees,”Proceedings of Symposium on Large
Spatial Databases,pp.142–61,Springer-V erlag,
Berlin1993.
[Niev84]J.Nievergelt,H.Hinterberger and K.C.Sevcik,“The Grid File:An Adaptable,Symmetric Mul-
tikey File Structure,”ACM TODS,V ol.9,No.1,
March1984.
[Robi81]J.T.Robinson,“The K-D-B-Tree:A Search Structure for Large Multidimensional Dynamic
Indexes,”Proc.1981ACM SIGMOD Conf.,
pp.10–18.[Sagi86]Y.Sagiv,“Concurrent Operations on B*-Trees with Overtaking,”Journal of Computer and Sys-
tem Sciences,V ol.33,No.2,pp.275–296,1986. [SrCa91]V.Srinivasan and M.Carey,“Performance of B-Tree Concurrency Control Algorithms,”Proc.
1991ACM SIGMOD conf.,pp.416–425. [SRF87]T.Sellis,N.Roussopoulos,and C.Faloutsos,“The R+-tree:A Dynamic Index for Multidimen-
sional Objects,”Proc.13th Int’l Conf.on V ery
Large Databases(VLDB),Sep.1987,pp.507–
518.
[Ston87]M.Stonebraker,“The Design of the Postgres Storage System,”Proc.13th Int’l Conf.on V ery
Large Databases(VLDB),Sep.1987.
商务公文写作目录 一、商务公文的基本知识 二、应把握的事项与原则 三、常用商务公文写作要点 四、常见错误与问题
一、商务公文的基本知识 1、商务公文的概念与意义 商务公文是商业事务中的公务文书，是企业在生产经营管理活动中产生的，按照严格的、既定的生效程序和规范的格式而制定的具有传递信息和记录作用的载体。规范严谨的商务文书，不仅是贯彻企业执行力的重要保障，而且已经成为现代企业管理的基础中不可或缺的内容。商务公文的水平也是反映企业形象的一个窗口，商务公文的写作能力常成为评价员工职业素质的重要尺度之一。 2、商务公文分类：（1）根据形成和作用的商务活动领域，可分为通用公文和专用公文两类（2）根据内容涉及秘密的程度，可分为对外公开、限国内公开、内部使用、秘密、机密、绝密六类（3）根据行文方向，可分为上行文、下行文、平行文三类（4）根据内容的性质，可分为规范性、指导性、公布性、陈述呈请性、商洽性、证明性公文（5）根据处理时限的要求，可分为平件、急件、特急件三类（6）根据来源，在一个部门内部可分为收文、发文两类。 3、常用商务公文： （1）公务信息：包括通知、通报、通告、会议纪要、会议记录等 （2）上下沟通：包括请示、报告、公函、批复、意见等 （3）建规立矩：包括企业各类管理规章制度、决定、命令、任命等； （4）包容大事小情：包括简报、调查报告、计划、总结、述职报告等； （5）对外宣传：礼仪类应用文、领导演讲稿、邀请函等； （6）财经类：经济合同、委托授权书等； （7）其他：电子邮件、便条、单据类（借条、欠条、领条、收条）等。 考虑到在座的主要岗位，本次讲座涉及请示、报告、函、计划、总结、规章制度的写作，重点谈述职报告的写作。 4、商务公文的特点： （1）制作者是商务组织。（2）具有特定效力，用于处理商务。 （3）具有规范的结构和格式，而不像私人文件靠“约定俗成”的格式。商务公文区别于其它文章的主要特点是具有法定效力与规范格式的文件。 5、商务公文的四个构成要素： （1）意图：主观上要达到的目标 (2）结构：有效划分层次和段落，巧设过渡和照应 (3）材料：组织材料要注意多、细、精、严 (4) 正确使用专业术语、熟语、流行语等词语，适当运用模糊语言、模态词语与古词语。 6、基本文体与结构 商务文体区别于其他文体的特殊属性主要有直接应用性、全面真实性、结构格式的规范性。其特征表现为：被强制性规定采用白话文形式，兼用议论、说明、叙述三种基本表达方法。商务公文的基本组成部分有：标题、正文、作者、日期、印章或签署、主题词。其它组成部分有文头、发文字号、签发人、保密等级、紧急程度、主送机关、附件及其标记、抄送机关、注释、印发说明等。印章或签署均为证实公文作者合法性、真实性及公文效力的标志。 7、稿本 （1）草稿。常有“讨论稿”“征求意见稿”“送审稿”“草稿”“初稿”“二稿”“三稿”等标记。（2）定稿。是制作公文正本的标准依据。有法定的生效标志（签发等）。（3）正本。格式正规并有印章或签署等表明真实性、权威性、有效性。（4）试行本。在试验期间具有正式公文的法定效力。（5）暂行本。在规定
关于会议纪要的规范格式和写作要求 一、会议纪要的概念 会议纪要是一种记载和传达会议基本情况或主要精神、议定事项等内容的规定性公文。是在会议记录的基础上，对会议的主要内容及议定的事项，经过摘要整理的、需要贯彻执行或公布于报刊的具有纪实性和指导性的文件。 会议纪要根据适用范围、内容和作用，分为三种类型： 1、办公会议纪要(也指日常行政工作类会议纪要)，主要用于单位开会讨论研究问题，商定决议事项，安排布置工作，为开展工作提供指导和依据。如，xx学校工作会议纪要、部长办公会议纪要、市委常委会议纪要。 2、专项会议纪要(也指协商交流性会议纪要)，主要用于各类交流会、研讨会、座谈会等会议纪要，目的是听取情况、传递信息、研讨问题、启发工作等。如，xx县脱贫致富工作座谈会议纪要。 3、代表会议纪要(也指程序类会议纪要)。它侧重于记录会议议程和通过的决议，以及今后工作的建议。如《××省第一次盲人聋哑人代表会议纪要》、《xx市第x次代表大会会议纪要》。 另外，还有工作汇报、交流会，部门之间的联席会等方面的纪要，但基本上都系日常工作类的会议纪要。 二、会议纪要的格式 会议纪要通常由标题、正文、结尾三部分构成。
1、标题有三种方式：一是会议名称加纪要，如《全国农村工作会议纪要》;二是召开会议的机关加内容加纪要，也可简化为机关加纪要，如《省经贸委关于企业扭亏会议纪要》、《xx组织部部长办公会议纪要》;三是正副标题相结合，如《维护财政制度加强经济管理——在xx部门xx座谈会上的发言纪要》。 会议纪要应在标题的下方标注成文日期，位置居中，并用括号括起。作为文件下发的会议纪要应在版头部分标注文号，行文单位和成文日期在文末落款(加盖印章)。 2、会议纪要正文一般由两部分组成。 (1)开头，主要指会议概况，包括会议时间、地点、名称、主持人，与会人员，基本议程。 (2)主体，主要指会议的精神和议定事项。常务会、办公会、日常工作例会的纪要，一般包括会议内容、议定事项，有的还可概述议定事项的意义。工作会议、专业会议和座谈会的纪要，往往还要写出经验、做法、今后工作的意见、措施和要求。 (3)结尾，主要是对会议的总结、发言评价和主持人的要求或发出的号召、提出的要求等。一般会议纪要不需要写结束语，主体部分写完就结束。 三、会议纪要的写法 根据会议性质、规模、议题等不同，正文部分大致可以有以下几种写法： 1、集中概述法(综合式)。这种写法是把会议的基本情况，讨
毕业论文写作要求与格式规范 关于《毕业论文写作要求与格式规范》，是我们特意为大家整理的，希望对大家有所帮助。 (一)文体 毕业论文文体类型一般分为：试验论文、专题论文、调查报告、文献综述、个案评述、计算设计等。学生根据自己的实际情况，可以选择适合的文体写作。 (二)文风 符合科研论文写作的基本要求：科学性、创造性、逻辑性、
实用性、可读性、规范性等。写作态度要严肃认真，论证主题应有一定理论或应用价值;立论应科学正确，论据应充实可靠，结构层次应清晰合理，推理论证应逻辑严密。行文应简练，文笔应通顺，文字应朴实，撰写应规范，要求使用科研论文特有的科学语言。 (三)论文结构与排列顺序 毕业论文，一般由封面、独创性声明及版权授权书、摘要、目录、正文、后记、参考文献、附录等部分组成并按前后顺序排列。 1.封面：毕业论文(设计)封面具体要求如下： (1)论文题目应能概括论文的主要内容，切题、简洁，不超过30字，可分两行排列;
(2)层次：大学本科、大学专科 (3)专业名称：机电一体化技术、计算机应用技术、计算机网络技术、数控技术、模具设计与制造、电子信息、电脑艺术设计、会计电算化、商务英语、市场营销、电子商务、生物技术应用、设施农业技术、园林工程技术、中草药栽培技术和畜牧兽医等专业，应按照标准表述填写; (4)日期：毕业论文(设计)完成时间。 2.独创性声明和关于论文使用授权的说明：需要学生本人签字。 3.摘要：论文摘要的字数一般为300字左右。摘要是对论文的内容不加注释和评论的简短陈述，是文章内容的高度概括。主要内容包括：该项研究工作的内容、目的及其重要性;所使用的实验方法;总结研究成果，突出作者的新见解;研究结论及其意义。摘要中不列举例证，不描述研究过程，不做自我评价。
公文格式规范与常见公文写作 一、公文概述与公文格式规范 党政机关公文种类的区分、用途的确定及格式规范等，由中共中央办公厅、国务院办公厅于2012年4月16日印发，2012年7月1日施行的《党政机关公文处理工作条例》规定。之前相关条例、办法停止执行。 （一）公文的含义 公文，即公务文书的简称，属应用文。 广义的公文，指党政机关、社会团体、企事业单位，为处理公务按照一定程序而形成的体式完整的文字材料。 狭义的公文，是指在机关、单位之间，以规范体式运行的文字材料，俗称“红头文件”。 ?（二）公文的行文方向和原则 ?、上行文下级机关向上级机关行文。有“请示”、“报告”、和“意见”。 ?、平行文同级机关或不相隶属机关之间行文。主要有“函”、“议案”和“意见”。 ?、下行文上级机关向下级机关行文。主要有“决议”、“决定”、“命令”、“公报”、“公告”、“通告”、“意见”、“通知”、“通报”、“批复”和“会议纪要”等。 ?其中，“意见”、“会议纪要”可上行文、平行文、下行文。?“通报”可下行文和平行文。 ?原则： ?、根据本机关隶属关系和职权范围确定行文关系 ?、一般不得越级行文 ?、同级机关可以联合行文 ?、受双重领导的机关应分清主送机关和抄送机关 ?、党政机关的部门一般不得向下级党政机关行文 ?(三) 公文的种类及用途 ?、决议。适用于会议讨论通过的重大决策事项。 ?、决定。适用于对重要事项作出决策和部署、奖惩有关单位和人员、变更或撤销下级机关不适当的决定事项。
?、命令（令）。适用于公布行政法规和规章、宣布施行重大强制性措施、批准授予和晋升衔级、嘉奖有关单位和人员。 ?、公报。适用于公布重要决定或者重大事项。 ?、公告。适用于向国内外宣布重要事项或者法定事项。 ?、通告。适用于在一定范围内公布应当遵守或者周知的事项。?、意见。适用于对重要问题提出见解和处理办法。 ?、通知。适用于发布、传达要求下级机关执行和有关单位周知或者执行的事项，批转、转发公文。 ?、通报。适用于表彰先进、批评错误、传达重要精神和告知重要情况。 ?、报告。适用于向上级机关汇报工作、反映情况，回复上级机关的询问。 ?、请示。适用于向上级机关请求指示、批准。 ?、批复。适用于答复下级机关请示事项。 ?、议案。适用于各级人民政府按照法律程序向同级人民代表大会或者人民代表大会常务委员会提请审议事项。 ?、函。适用于不相隶属机关之间商洽工作、询问和答复问题、请求批准和答复审批事项。 ?、纪要。适用于记载会议主要情况和议定事项。?（四）、公文的格式规范 ?、眉首的规范 ?（）、份号 ?也称编号，置于公文首页左上角第行，顶格标注。“秘密”以上等级的党政机关公文，应当标注份号。 ?（）、密级和保密期限 ?分“绝密”、“机密”、“秘密”三个等级。标注在份号下方。?（）、紧急程度 ?分为“特急”和“加急”。由公文签发人根据实际需要确定使用与否。标注在密级下方。 ?（）、发文机关标志（或称版头） ?由发文机关全称或规范化简称加“文件”二字组成。套红醒目，位于公文首页正中居上位置（按《党政机关公文格式》标准排
学生会文档书写格式规范要求 目前各部门在日常文书编撰中大多按照个人习惯进行排版，文档中字体、文字大小、行间距、段落编号、页边距、落款等参数设置不规范，严重影响到文书的标准性和美观性，以下是文书标准格式要求及日常文档书写注意事项，请各部门在今后工作中严格实行： 一、文件要求 1.文字类采用Word格式排版 2.统计表、一览表等表格统一用Excel格式排版 3.打印材料用纸一般采用国际标准A4型（210mm×297mm），左侧装订。版面方向以纵向为主，横向为辅，可根据实际需要确定 4.各部门的职责、制度、申请、请示等应一事一报，禁止一份行文内同时表述两件工作。 5.各类材料标题应规范书写，明确文件主要内容。 二、文件格式 （一）标题 1.文件标题：标题应由发文机关、发文事由、公文种类三部分组成，黑体小二号字,不加粗，居中，段后空1行。 （二）正文格式 1. 正文字体：四号宋体，在文档中插入表格，单元格内字体用宋体，字号可根据内容自行设定。 2.页边距：上下边距为2.54厘米；左右边距为 3.18厘米。
3.页眉、页脚：页眉为1.5厘米；页脚为1.75厘米； 4.行间距：1.5倍行距。 5.每段前的空格请不要使用空格，应该设置首先缩进2字符 6.年月日表示：全部采用阿拉伯数字表示。 7.文字从左至右横写。 （三）层次序号 (1)一级标题：一、二、三、 (2)二级标题：（一）（二）（三） (3)三级标题：1. 2. 3. (4)四级标题：（1）（2）（3） 注：三个级别的标题所用分隔符号不同，一级标题用顿号“、”例如：一、二、等。二级标题用括号不加顿号，例如：（三）（四）等。三级标题用字符圆点“.”例如：5. 6.等。 （四）、关于落款： 1.对外行文必须落款“湖南环境生物专业技术学院学生会”“校学生会”各部门不得随意使用。 2.各部门文件落款需注明组织名称及部门“湖南环境生物专业技术学院学生会XX部”“校学生会XX部” 3.所有行文落款不得出现“环境生物学院”“湘环学院”“学生会”等表述不全的简称。 4.落款填写至文档末尾右对齐，与前一段间隔2行 5.时间落款：文档中落款时间应以“2016年5月12日”阿拉伯数字
政府公文写作格式 一、眉首部分 （一）发文机关标识 平行文和下行文的文件头，发文机关标识上边缘至上页边为62mm，发文机关下边缘至红色反线为28mm。 上行文中，发文机关标识上边缘至版心上边缘为80mm，即与上页边距离为117mm，发文机关下边缘至红色反线为30mm。 发文机关标识使用字体为方正小标宋_GBK，字号不大于22mm×15mm。 （二）份数序号 用阿拉伯数字顶格标识在版心左上角第一行，不能少于2位数。标识为“编号000001” （三）秘密等级和保密期限 用3号黑体字顶格标识在版心右上角第一行，两字中间空一字。如需要加保密期限的，密级与期限间用“★”隔开，密级中则不空字。 （四）紧急程度 用3号黑体字顶格标识在版心右上角第一行，两字中间空一字。如同时标识密级，则标识在右上角第二行。 （五）发文字号 标识在发文机关标识下两行，用3号方正仿宋_GBK字体剧
中排布。年份、序号用阿拉伯数字标识，年份用全称，用六角括号“〔〕”括入。序号不用虚位，不用“第”。发文字号距离红色反线4mm。 （六）签发人 上行文需要标识签发人，平行排列于发文字号右侧，发文字号居左空一字，签发人居右空一字。“签发人”用3号方正仿宋_GBK，后标全角冒号，冒号后用3号方正楷体_GBK标识签发人姓名。多个签发人的，主办单位签发人置于第一行，其他从第二行起排在主办单位签发人下，下移红色反线，最后一个签发人与发文字号在同一行。 二、主体部分 （一）标题 由“发文机关+事由+文种”组成，标识在红色反线下空两行，用2号方正小标宋_GBK，可一行或多行居中排布。 （二）主送机关 在标题下空一行，用3号方正仿宋_GBK字体顶格标识。回行是顶格，最后一个主送机关后面用全角冒号。 （三）正文 主送机关后一行开始，每段段首空两字，回行顶格。公文中的数字、年份用阿拉伯数字，不能回行，阿拉伯数字：用3号Times New Roman。正文用3号方正仿宋_GBK，小标题按照如下排版要求进行排版：
广州中医药大学研究生学位论文基本要求与写作规范 为了进一步提高学位工作水平和学位论文质量，保证我校学位论文在结构和格式上的规范与统一，特做如下规定： 一、学位论文基本要求 （一）科学学位硕士论文要求 1.论文的基本科学论点、结论，应在中医药学术上和中医药科学技术上具有一定的理论意义和实践价值。 2.论文所涉及的内容，应反映出作者具有坚实的基础理论和系统的专门知识。 3.实验设计和方法比较先进，并能掌握本研究课题的研究方法和技能。 4.对所研究的课题有新的见解。 5.在导师指导下研究生独立完成。 6.论文字数一般不少于3万字，中、英文摘要1000字左右。 （二）临床专业学位硕士论文要求 临床医学硕士专业学位申请者在临床科研能力训练中学会文献检索、收集资料、数据处理等科学研究的基本方法,培养临床思维能力与分析能力,完成学位论文。 1.学位论文包括病例分析报告及文献综述。 2.学位论文应紧密结合中医临床或中西结合临床实际，以总结临床实践经验为主。 3.学位论文应表明申请人已经掌握临床科学研究的基本方法。 4.论文字数一般不少于15000字，中、英文摘要1000字左右。 （三）科学学位博士论文要求 1.研究的课题应在中医药学术上具有较大的理论意义和实践价值。 2.论文所涉及的内容应反映作者具有坚实宽广的理论基础和系统深入的专门知识，并表明作者具有独立从事科学研究工作的能力。 3.实验设计和方法在国内同类研究中属先进水平，并能独立掌握本研究课题的研究方法和技能。
4.对本研究课题有创造性见解，并取得显著的科研成果。 5.学位论文必须是作者本人独立完成，与他人合作的只能提出本人完成的部分。 6.论文字数不少于5万字，中、英摘要3000字；详细中文摘要(单行本)1万字左右。 （四）临床专业学位博士论文要求 1.要求论文课题紧密结合中医临床或中西结合临床实际，研究结果对临床工作具有一定的应用价值。 2.论文表明研究生具有运用所学知识解决临床实际问题和从事临床科学研究的能力。 3.论文字数一般不少于3万字，中、英文摘要2000字；详细中文摘要(单行本)5000字左右。 二、学位论文的格式要求 （一）学位论文的组成 博士、硕士学位论文一般应由以下几部分组成，依次为：1.论文封面；2. 原创性声明及关于学位论文使用授权的声明；3.中文摘要；4.英文摘要；5.目录； 6.引言； 7.论文正文； 8.结语； 9.参考文献；10.附录；11.致谢。 1.论文封面：采用研究生处统一设计的封面。论文题目应以恰当、简明、引人注目的词语概括论文中最主要的内容。避免使用不常见的缩略词、缩写字，题名一般不超过30个汉字。论文封面“指导教师”栏只写入学当年招生简章注明、经正式遴选的指导教师1人，协助导师名字不得出现在论文封面。 2．原创性声明及关于学位论文使用授权的声明（后附）。 3.中文摘要：要说明研究工作目的、方法、成果和结论。并写出论文关键词3～5个。 4.英文摘要：应有题目、专业名称、研究生姓名和指导教师姓名，内容与中文提要一致，语句要通顺，语法正确。并列出与中文对应的论文关键词3～5个。 5.目录：将论文各组成部分(1～3级)标题依次列出，标题应简明扼要，逐项标明页码，目录各级标题对齐排。 6.引言：在论文正文之前，简要说明研究工作的目的、范围、相关领域前人所做的工作和研究空白，本研究理论基础、研究方法、预期结果和意义。应言简
（公文写作）毕业论文写作要求和格式规范
中国农业大学继续教育学院 毕业论文写作要求和格式规范 壹、写作要求 （壹）文体 毕业论文文体类型壹般分为：试验论文、专题论文、调查方案、文献综述、个案评述、计算设计等。学生根据自己的实际情况，能够选择适合的文体写作。 （二）文风 符合科研论文写作的基本要求：科学性、创造性、逻辑性、实用性、可读性、规范性等。写作态度要严肃认真，论证主题应有壹定理论或应用价值；立论应科学正确，论据应充实可靠，结构层次应清晰合理，推理论证应逻辑严密。行文应简练，文笔应通顺，文字应朴实，撰写应规范，要求使用科研论文特有的科学语言。 （三）论文结构和排列顺序 毕业论文，壹般由封面、独创性声明及版权授权书、摘要、目录、正文、后记、参考文献、附录等部分组成且按前后顺序排列。 1．封面：毕业论文（设计）封面（见文件5）具体要求如下： （1）论文题目应能概括论文的主要内容，切题、简洁，不超过30字，可分俩行排列； （2）层次：高起本，专升本，高起专； （3）专业名称：现开设园林、农林经济管理、会计学、工商管理等专业，应按照标准表述填写； （4）密级：涉密论文注明相应保密年限； （5）日期：毕业论文完成时间。 2．独创性声明和关于论文使用授权的说明：（略）。
3．摘要：论文摘要的字数壹般为300字左右。摘要是对论文的内容不加注释和评论的简短陈述，是文章内容的高度概括。主要内容包括：该项研究工作的内容、目的及其重要性；所使用的实验方法；总结研究成果，突出作者的新见解；研究结论及其意义。摘要中不列举例证，不描述研究过程，不做自我评价。 论文摘要后另起壹行注明本文的关键词，关键词是供检索用的主题词条，应采用能够覆盖论文内容的通用专业术语，符合学科分类，壹般为3～5个，按照词条的外延层次从大到小排列。 4．目录（目录示例见附件3）：独立成页，包括论文中的壹级、二级标题、后记、参考文献、和附录以及各项所于的页码。 5．正文：包括前言、论文主体和结论 前言：为正文第壹部分内容，简单介绍本项研究的背景和国内外研究成果、研究现状，明确研究目的、意义以及要解决的问题。 论文主体：是全文的核心部分，于正文中应将调查、研究中所得的材料和数据加工整理和分析研究，提出论点，突出创新。内容可根据学科特点和研究内容的性质而不同。壹般包括：理论分析、计算方法、实验装置和测试方法、对实验结果或调研结果的分析和讨论，本研究方法和已有研究方法的比较等方面。内容要求论点正确，推理严谨，数据可靠，文字精炼，条理分明，重点突出。 结论：为正文最后壹部分，是对主要成果的归纳和总结，要突出创新点，且以简练的文字对所做的主要工作进行评价。 6．后记：对整个毕业论文工作进行简单的回顾总结，对给予毕业论文工作提供帮助的组织或个人表示感谢。内容应尽量简单明了，壹般为200字左右。 7．参考文献：是论文不可或缺的组成部分。它既可反映毕业论文工作中取材广博程度，又可反映文稿的科学依据和作者尊重他人研究成果的严肃态度，仍能够向读者提供有关
【最新整理，下载后即可编辑】 广东工业大学成人高等教育 本科生毕业论文格式规范（摘录整理） 一、毕业论文完成后应提交的资料 最终提交的毕业论文资料应由以下部分构成： （一）毕业论文任务书（一式两份，与论文正稿装订在一起）（二）毕业论文考核评议表（一式三份，学生填写表头后发电子版给老师） （三）毕业论文答辩记录（一份, 学生填写表头后打印出来，答辩时使用） （四）毕业论文正稿（一式两份，与论文任务书装订在一起），包括以下内容： 1、封面 2、论文任务书 3、中、英文摘要（先中文摘要，后英文摘要，分开两页排版） 4、目录 5、正文（包括：绪论、正文主体、结论） 6、参考文献 7、致谢 8、附录（如果有的话） （五）论文任务书和论文正稿的光盘
二、毕业论文资料的填写与装订 毕业论文须用计算机打印，一律使用A4打印纸，单面打印。 毕业论文任务书、毕业论文考核评议表、毕业论文正稿、答辩纪录纸须用计算机打印，一律使用A4打印纸。答辩提问记录一律用黑色或蓝黑色墨水手写，要求字体工整，卷面整洁；任务书由指导教师填写并签字，经主管院领导签字后发出。 毕业论文使用统一的封面，资料装订顺序为：毕业论文封面、论文任务书、考核评议表、答辩记录、中文摘要、英文摘要、目录、正文、参考文献、致谢、附录（如果有的话）。论文封面要求用A3纸包边。 三、毕业论文撰写的内容与要求 一份完整的毕业论文正稿应包括以下几个方面： （一）封面（见封面模版） （二）论文题目（填写在封面上，题目使用2号隶书，写作格式见封面模版） 题目应简短、明确，主标题不宜超过20字；可以设副标题。（三）论文摘要（写作格式要求见《摘要、绪论、结论、参考文献写作式样》P1～P2） 1、中文“摘要”字体居中，独占一页
论文的写作格式及规范
附件9： 科学技术论文的写作格式及规范 用非公知公用的缩写词、字符、代号，尽量不出现数学式和化学式。 2作者署名和工作单位标引和检索，根据国家有关标准、数据规范为了提高技师、高级技师论文的学术质量，实现论文写的科学化、程序化和规范化，以利于科技信息的传递和科技情报的作评定工作，特制定本技术论文的写作格式及规范。望各位学员在注重科学研究的同时，做好科技论文撰写规范化工作。 1 题名 题名应以简明、确切的词语反映文章中最重要的特定内容，要符合编制题录、索引和检索的有关原则，并有助于选定关键词。 中文题名一般不宜超过20 个字，必要时可加副题名。英文题名应与中文题名含义一致。 题名应避免使作者署名是文责自负和拥有著作权的标志。作者姓名署于题名下方，团体作者的执笔人也可标注于篇首页地脚或文末，简讯等短文的作者可标注于文末。 英文摘要中的中国人名和地名应采用《中国人名汉语拼音字母拼写法》的有关规定；人名姓前名后分写，姓、名的首字母大写，名字中间不加连字符；地名中的专名和通名分写，每分写部分的首字母大写。 作者应标明其工作单位全称、省及城市名、邮编( 如“齐齐哈尔电业局黑龙江省齐齐哈尔市(161000) ”)，同时，在篇首页地脚标注第一作者的作者简介，内容包括姓名，姓别，出生年月，学位，职称，研究成果及方向。
3摘要 论文都应有摘要（3000 字以下的文章可以略去）。摘要的：写作应符合GB6447-86的规定。摘要的内容包括研究的目的、方法、结果和结论。一般应写成报道性文摘，也可以写成指示性或报道-指示性文摘。摘要应具有独立性和自明性，应是一篇完整的短文。一般不分段，不用图表和非公知公用的符号或术语，不得引用图、表、公式和参考文献的序号。中文摘要的篇幅：报道性的300字左右，指示性的100 字左右，报道指示性的200字左右。英文摘要一般与中文摘要内容相对应。 4关键词 关键词是为了便于作文献索引和检索而选取的能反映论文主题概念的词或词组，一般每篇文章标注3?8个。关键词应尽量从《汉语主题词表》等词表中选用规范词——叙词。未被词表收录的新学科、新技术中的重要术语和地区、人物、文献、产品及重要数据名称，也可作为关键词标出。中、英文关键词应一一对应。 5引言 引言的内容可包括研究的目的、意义、主要方法、范围和背景等。 应开门见山，言简意赅，不要与摘要雷同或成为摘要的注释，避免公式推导和一般性的方法介绍。引言的序号可以不写，也可以写为“ 0”，不写序号时“引言”二字可以省略。 6论文的正文部分 论文的正文部分系指引言之后，结论之前的部分，是论文的核心， 应按GB7713--87 的规定格式编写。 6.1层次标题
沈阳农业大学本科生毕业论文（设计）撰写要求与格式规范 （2008年7月修订） 毕业论文（设计）是培养学生综合运用所学知识 分析和解决实际问题 提高实践能力和创造能力的重要教学环节 是记录科学研究成果的重要文献 也是学生申请学位的基本依据 为保证本科生毕业论文（设计）质量 促进国内外学术交流 特制定《沈阳农业大学本科生毕业论文（设计）撰写要求与格式规范》 一、毕业论文（设计）的基本结构 毕业论文（设计）的基本结构是： 1.前置部分：包括封面、任务书、选题审批表、指导记录、考核表、中（外）文摘要、关键词和目录等 2.主体部分：包括前言、正文、参考文献、附录和致谢等 二、毕业论文（设计）的内容要求 （一）前置部分 1．封面 由学校统一设计 2．毕业论文（设计）任务书 毕业论文（设计）任务由各教学单位负责安排 并根据已确定的论文（设计）课题下达给学生 作为学生和指导教师共同从事毕业论文（设计）工作的依据 毕业论文（设计）任务书的内容包括课题名称、学生姓名、下发日期、论文（设计）的主要内容与要求、毕业论文（设计）的工作进度和起止时间等 3．论文（设计）选题审批表 4．论文（设计）指导记录 5．毕业论文（设计）考核表 指导教师评语、评阅人评审意见分别由指导教师和评阅人填写 答辩委员会意见、评定成绩以及是否授予学士学位的建议等材料应由答辩委员会填写 6．中（外）文摘要 摘要是毕业论文（设计）研究内容及结论的简明概述 具有独立性和自含性 其内容包括论文（设计）的主要内容、试（实）验方法、结果、结论和意义等 中文摘要不少于400字；英文摘要必须用第三人称 采用现在时态编写 7．关键词
广东工业大学成人高等教育 本科生毕业论文格式规范（摘录整理） 一、毕业论文完成后应提交的资料 最终提交的毕业论文资料应由以下部分构成： （一）毕业论文任务书（一式两份，与论文正稿装订在一起） （二）毕业论文考核评议表（一式三份，学生填写表头后发电子版给老师） （三）毕业论文答辩记录（一份, 学生填写表头后打印出来，答辩时使用） （四）毕业论文正稿（一式两份，与论文任务书装订在一起），包括以下内容： 1、封面 2、论文任务书 3、中、英文摘要（先中文摘要，后英文摘要，分开两页排版） 4、目录 5、正文（包括：绪论、正文主体、结论） 6、参考文献 7、致谢 8、附录（如果有的话） （五）论文任务书和论文正稿的光盘 二、毕业论文资料的填写与装订 毕业论文须用计算机打印，一律使用A4打印纸，单面打印。 毕业论文任务书、毕业论文考核评议表、毕业论文正稿、答辩纪录纸须用计算机打印，一律使用A4打印纸。答辩提问记录一律用黑色或蓝黑色墨水手写，要求字体工整，卷面整洁；任务书由指导教师填写并签字，经主管院领导签字后发出。 毕业论文使用统一的封面，资料装订顺序为：毕业论文封面、论文任务书、考核评议表、答辩记录、中文摘要、英文摘要、目录、正文、参考文献、致谢、附录（如果有的话）。论文封面要求用A3纸包边。
三、毕业论文撰写的内容与要求 一份完整的毕业论文正稿应包括以下几个方面： （一）封面（见封面模版） （二）论文题目（填写在封面上，题目使用2号隶书，写作格式见封面模版）题目应简短、明确，主标题不宜超过20字；可以设副标题。 （三）论文摘要（写作格式要求见《摘要、绪论、结论、参考文献写作式样》P1～P2） 1、中文“摘要”字体居中，独占一页 2、论文摘要应能概括研究题目的内容和主要观点，中文摘要在400字左右， 并翻译成英文。 3、关键词是供检索用的主题词条，应采用能覆盖论文主要内容的通用技术词条。关键词一般为3～5个，按词条的外延层次排列（外延大的排在前面）。 4、英文摘要另起一页，其内容应与中文摘要一致，并要符合英语语法，语句通顺，文字流畅。英文和汉语拼音一律为Times New Roman体，字号与中文摘要同。（四）目录（写作格式要求见《论文提纲写作提示》） 目录应独占一页。目录按三级标题编写（即：1……、1.1……、1.1.1……），要求标题层次清晰。目录中的标题应与正文中的标题一致。 （五）正文 论文正文分章节撰写, 每章标题（即一级标题）应另起一页，标题采用3号黑体加粗，居于页面第一行中间。标题的段后需加宽0.5行。 毕业论文正文包括绪论、正文主体与结论。其内容分别如下： 1、绪论(写作格式要求见《摘要、绪论、结论、参考文献写作式样》P3) “绪论”二字需居于页面第一行中间。采用3号黑体加粗，标题段后需加宽0.5行。 绪论应说明本课题的目的、意义、研究范围及要达到的技术要求；简述本题目在国内外的发展概况及存在的问题；说明本课题的指导思想；阐述本题目应解决的主要问题。 2、正文主体 (写作格式要求见《摘要、绪论、结论、参考文献写作式样》P3) 正文主体是对研究工作的详细表述，其内容包括：问题的提出，研究工作的基本前提、假设和条件；基本概念和理论基础；问题的分析，理论的论证；运用某些理论对
英语论文格式及写作规范 语言和内容是评判一篇英语论文质量高低的重要依据；但是,写作格式规范与否亦是一个不可忽略的衡量标准。因此,规范英语论文的格式,使之与国际学术惯例接轨,对我们从事英语教学,英语论文写作,促进国际学术交流都具有重要意义。由于英语论文写作规范随学科不同而各有所异,本文拟就人文类学科英语论文的主要组成部分,概述美国教育界、学术界通行的人文类英语论文写作规范,以供读者参考、仿效。 一、英语论文的标题 一篇较长的英语论文(如英语毕业论文)一般都需要标题页,其书写格式如下:第一行标题与打印纸顶端的距离约为打印纸全长的三分之一,与下行(通常为by,居中)的距离则为5cm,第三、第四行分别为作者姓名及日期(均居中)。如果该篇英语论文是学生针对某门课程而写,则在作者姓名与日期之间还需分别打上教师学衔及其姓名(如:Dr./Prof.C.Prager)及本门课程的编号或名称(如:English 734或British Novel)。打印时,如无特殊要求,每一行均需double space,即隔行打印,行距约为0.6cm(论文其他部分行距同此)。 就学生而言,如果英语论文篇幅较短,亦可不做标题页(及提纲页),而将标题页的内容打在正文第一页的左上方。第一行为作者姓名,与打印纸顶端距离约为2.5cm,以下各行依次为教师学衔和姓、课程编号(或名称)及日期；各行左边上下对齐,并留出2.5cm左右的页边空白(下同)。接下来便是论文标题及正文(日期与标题之间及标题与正文第一行之间只需隔行打印,不必留出更多空白)。 二、英语论文提纲 英语论文提纲页包括论题句及提纲本身,其规范格式如下:先在第一行(与打印纸顶端的距离仍为2.5cm左右)的始端打上Thesis 一词及冒号,空一格后再打论题句,回行时左边须与论题句的第一个字母上下对齐。主要纲目以大写罗马数字标出,次要纲目则依次用大写英文字母、阿拉伯数字和小写英文字母标出。各数字或字母后均为一句点,空出一格后再打该项内容的第一个字母；处于同一等级的纲目,其上下行左边必须对齐。需要注意的是,同等重要的纲目必须是两个以上,即:有Ⅰ应有Ⅱ,有A应有B,以此类推。如果英文论文提纲较长,需两页纸,则第二页须在右上角用小写罗马数字标出页码,即ii(第一页无需标页码)。 三、英语论文正文 有标题页和提纲页的英语论文,其正文第一页的规范格式为:论文标题居中,其位置距打印纸顶端约5cm,距正文第一行约1.5cm。段首字母须缩进五格,即从第六格打起。正文第一页不必标页码(但应计算其页数),自第二页起,必须在每页的右上角(即空出第一行,在其后部)打上论文作者的姓,空一格后
中国石油大学（北京）远程教育学院 英语专业毕业设计（论文）写作要求与格式规范 一、总体要求 1.英语本科学生毕业设计(论文)应规范、完整，符合英语语言文学系的规范性规定和要求。论文和毕业设计手册采用B5纸单面打印，页边距上、下、左、右均为 2.2厘米。 2.论文封面填写 英语专业毕业设计（论文）共包括汉、英语两个封面。汉语封面为外层装订封面，选用白色光面纸张，英语封面为内衬封面，选用一般打印纸张。封面填写应该明晰、规范，按照学院设计的固定格式填写。<详细情况请参阅《英语专业论文写作要求与格式》及本规定中“具体内容和撰写要求”部分有关封面填写的规定和说明。> 3.行距设置 毕业论文内容及各种标题（包括摘要、目录、致谢、参考文献、附录）的行距设置统一选用双倍行距，每页18行。 4.字体设置 [1]“Abstract”、“Table of Contents”、“Bibliography”、“Acknowledgements”等题目选用“三号加粗Times New Roman”、行距为双倍行距,跟其后内容空一行； [2] 正文第一级标题（如Chapter One等）选用“三号加粗Times New Roman”、行距为双倍行距,跟其后内容空一行；第二级标题选用“小三号加粗Times New Roman”；第三级标题选用“四号加粗Times New Roman”；摘要、目录、正文、致谢、参考文献等部分的正文内容统一选用“四号Times New Roman”、行距为双倍行距。（注意：为论证起见而选用的汉语实例和语料等，字体选用小四号宋体）各级标题第一个词的首字母和其他实词首字母大写。除一级标题为下空一行,与正文内容分开外，二级和三级标题均不再空行。各级标题应使用自动生成标题。各部分正文首行缩进4个字符。 5.正文撰写要求 正文分章节撰写，第一级标题用“Chapter One”、“Chapter Two”、“Chapter Three”等连续编号，每章结束应另起一页，标题末尾不加标点（问号、叹号、省略号除外），标题居中排列，如标题较长需要换行，则要求使用自动换行居中，下空一行接写第二级标题。
毕业设计统一格式规范 一、纸张和页面要求 A4纸打印；页边距上下各为2 厘米，左为边距3厘米，右边距为2厘米；所有字符间距为默认值（缩放100%，间距：标准）。 二、毕业设计说明书封面要求 采用统一规范，详见《毕业设计封面模板》。 三、正文内容格式要求（详见统一模板） 采用统一规范，详见《毕业设计模板》 四、正文、图、表格、公式撰写格式要求 1．全文的正文要标明章节，图、表和公式要按章编号，公式应另起一行书写，并按章编号。 2．按照正式出版物的惯例，章节目序号的级序规定如下：一、（一）、1、1）3．公式。公式的编号用圆括号括起，放在公式的右边行末，在公式和编号之间不加虚线。 4．表格。每个表格都应有自己的标题和序号。序号写在左方，不加标点，空一格接写标题，标题末尾不加标点。标题在表格上方正中。 5．图。每幅插图都应有自己的标题和序号。序号和标题写在图的下方正中。由若干分图组成的插图，分图用a、b、c……标序。
参考文献（四号黑体） （空一行） 期刊文献书写示例： 作者.论文篇名[J]（期刊文献都在论文篇名后加“［J］”）.刊物名.出版年，卷（期）：论文在刊物中的页码A～B 如： [1]高曙名.自动特征识特技术综述[J].计算机学报，1998，21（3）：281～288 图书文献书写示范：（中英文图书格式统一） 作者.书名[M]（图书文献都在论文篇名后加“[M]”）.出版社，出版年 如： [2]李道国，高永如.企业购并策略和案例分析[M].第2版.北京：中国农业出版社，2001 电子文献书写示范 作者.电子文献题名.出版者或网址，发表时间 （参考文献内容小五号宋体，不加粗，每行行距固定值22磅）
小学数学作业规范 总的要求： 作业布置与批改应做到“有布置必先做，有发必收，有收必改，有改必评，有错必纠”。 一、作业布置 1．作业设计精心 作业设计要根据课程标准和教材要求，兼顾知识技能和过程方法，准确、全面覆盖相关知识点，突出重点，精心选择具有代表性、典型性、思考性、综合性的内容，使学生能举一反三，触类旁通。提倡根据不同对象设计不同程度和数量的作业，提倡贴近生活适时适量设计一些具有研究性、实践性、综合性等多样化的课外作业。 2．作业布置适当 作业布置要坚持“少而精”的原则。作业要符合学科特点，对完成作业的形式、时间和书写要有明确要求。凡布置给学生的作业，教师都必须提前做。严禁布置惩罚性作业。提倡分层、分类布置作业，满足不同层次学生的需要。 二、作业本外观要求 1．作业本封面的书写要统一。左上角写明作业的类别：“课堂练习”标为A；“错题集”标为B。本子封面上的四条横线上分别写明校名、班级、姓名、学号。补充习题、练习册根据本面要求书写。 2．作业本要保持整洁，做到无卷角、无破损，不随意撕本子和在作业本上乱写乱画。未经老师同意，不得随意更换作业本。 三、作业书写要求 1．三年级以下（含三年级上学期）的学生，书写作业用铅笔。 2．三年级以上（含三年级下学期）的学生，书写作业用蓝色钢笔，不准用圆珠笔、中性笔。同一班级且一本本子内须用同一种笔书写，不得混用，字的大小约占线格的三分之二。作图用铅笔。 3．每次作业第一行与上次作业老师批改日期行空一行，居中写上日期（如9月1日），第二行写题目，题号写在左侧题次栏里。 4．计算题和文字题要完整地抄题（包括题目要求），抄题时距题次格空一个字的距离（约半厘米，一个等号的长度），解题时按题型要求书写。 a.计算题：一步计算直接写等号。如要列竖式则写在横式下面正中间的地方。两步以上计算要用递等式，等号对齐、长度约半厘米。
和分析研究，提出论点，突出创新。内容可根据学科特点和研究内容的性质而不同。一般包括：理论分析、计算方法、实验装置和测试方法、对实验结果或调研结果的分析与讨论，本研究方法与已有研究方法的比较等方面。内容要求论点正确，推理严谨，数据可靠，文字精炼，条理分明，重点突出。 结论：为正文最后一部分，是对主要成果的归纳和总结，要突出创新点，并以简练的文字对所做的主要工作进行评价。 6．后记：对整个毕业论文工作进行简单的回顾总结，对给予毕业论文工作提供帮助的 组织或个人表示感谢。内容应尽量简单明了，一般为200字左右。 7．参考文献： 是论文不可或缺的组成部分。它既可反映毕业论文工作中取材广博 程度，又可反映文稿的科学依据和作者尊重他人研究成果的严肃态度，还可以向读者提供有 关信息的出处。 正文中应按顺序在引用参考文献处的文字右上角用［ ］标明，［ ］中序号应与“参考文献”中序号一致，结论之后刊出参考文献，并列出只限于作者亲自阅读过的近期发表或出版与本专业密切相关的学术著作和学术期刊文献。引用其它一些未公开发表和出版的参考资料也应注明资料来源，有确需要说明的可以在后记或附录中予以说明。 8．附录：凡不宜放在论文正文中，但又与论文确有作用的资料，如较为冗长的公式推 导、重复性或者辅助性数据图表、计算程序及有关说明等，可以编制成论文的附录。附录字数不计入论文文字数量。 二、版式、格式 毕业论文封面应使用学院统一规定的论文样式，（对于未按规范规定撰写的毕业论文， 最终成绩可相应的下调一个等级）。 1．论文开本及版芯 论文开本大小：A4纸（210mm ×297mm ）； 正文页面设置：左边距：30mm ，右边距：25mm ；上边距：30mm ，下边距：25mm 。 2．层次和标题 层次应清楚，标题应简明扼要，重点突出。论文分三级标题，各级标题具体格式如下： 一级标题：宋体，三号，加粗、段前、段后间距为1行，左对齐，单列一行；二级标题：宋体，四号，加粗、段前、段后间距为1行，左对齐，单列一行；三级标题：宋体，小四号，段前、段后间距为1行；左对齐，单列一行；上述段前、段后间距可适当调节，以便于控制正文合适的换页位置；正文一级及以 下子标题格式为：1；1.1；1.1.1。 其它标题或需突出的重点，可用五号黑体（或加粗），可单列一行，也可放在段首。 3．摘要： 摘要标题为宋体、三号，加粗，段前段后间距为一行、居中、单列一行； 摘要内容采用小四号宋体，行间距为20磅。 4.关键词：与摘要同处一页，位于摘要之后，另起一行以“关键词”开头（字体加粗） 、管路敷设技术通过管线不仅可以解决吊顶层配置不规范高中资料试卷问题，而且可保障各类管路习题到位。在管路敷设过程中，要加强看护关于管路高中资料试卷连接管口处理高中资料试卷弯扁度固定盒位置保护层防腐跨接地线弯曲半径标高等，要求技术交底。管线敷设技术包含线槽、管架等多项方式，为解决高中语文电气课件中管壁薄、接口不严等问题，合理利用管线敷设技术。线缆敷设原则：在分线盒处，当不同电压回路交叉时，应采用金属隔板进行隔开处理；同一线槽内，强电回路须同时切断习题电源，线缆敷设完毕，要进行检查和检测处理。、电气课件中调试对全部高中资料试卷电气设备，在安装过程中以及安装结束后进行 高中资料试卷调整试验；通电检查所有设备高中资料试卷相互作用与相互关系，根据生产工艺高中资料试卷要求，对电气设备进行空载与带负荷下高中资料试卷调控试验；对设备进行调整使其在正常工况下与过度工作下都可以正常工作；对于继电保护进行整核对定值，审核与校对图纸，编写复杂设备与装置高中资料试卷调试方案，编写重要设备高中资料试卷试验方案以及系统启动方案；对整套启动过程中高中资料试卷电气设备进行调试工作并且进行过关运行高中资料试卷技术指导。对于调试过程中高中资料试卷技术问题，作为调试人员，需要在事前掌握图纸资料、设备制造厂家出具高中资料试卷试验报告与相关技术资料，并且了解现场设备高中资料试卷布置情况与有关高中资料试卷电气系统接线等情况，然后根据规范与规程规定，制定设备调试高中资料试卷方案。 、电气设备调试高中资料试卷技术电力保护装置调试技术，电力保护高中资料试卷配置技术是指机组在进行继电保护高中资料试卷总体配置时，需要在最大限度内来确保机组高中资料试卷安全，并且尽可能地缩小故障高中资料试卷破坏范围，或者对某些异常高中资料试卷工况进行自动处理，尤其要避免错误高中资料试卷保护装置动作，并且拒绝动作，来避免不必要高中资料试卷突然停机。因此，电力高中资料试卷保护装置调试技术，要求电力保护装置做到准确灵活。对于差动保护装置高中资料试卷调试技术是指发电机一变压器组在发生内部故障时，需要进行外部电源高中资料试卷切除从而采用高中资料试卷主要保护装置。