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C H A P T E R2Introduction to the Relational ModelPractice Exercises2.1Consider the relational database of Figure??.What are the appropriateprimary keys?Answer:The answer is shown in Figure2.1,with primary keys under-lined.2.2Consider the foreign key constraint from the dept name attribute of in-structor to the department relation.Give examples of inserts and deletes tothese relations,which can cause a violation of the foreign key constraint.Answer:•Inserting a tuple:(10111,Ostrom,Economics,110,000)into the instructor table,where the department table does not have thedepartment Economics,would violate the foreign key constraint.•Deleting the tuple:(Biology,Watson,90000)from the department table,where at least one student or instructortuple has dept name as Biology,would violate the foreign key con-straint.employee(person name,street,city)works(person name company name,salary)company(company name,city)Figure2.1Relational database for Practice Exercise2.1.12Chapter2Introduction to the Relational Model2.3Consider the time slot relation.Given that a particular time slot can meetmore than once in a week,explain why day and start time are part of theprimary key of this relation,while end time is not.Answer:The attributes day and start time are part of the primary keysince a particular class will most likely meet on several different days,and may even meet more than once in a day.However,end time is notpart of the primary key since a particular class that starts at a particulartime on a particular day cannot end at more than one time.2.4In the instance of instructor shown in Figure??,no two instructors havethe same name.From this,can we conclude that name can be used as asuperkey(or primary key)of instructor?Answer:No.For this possible instance of the instructor table the namesare unique,but in general this may not be always the case(unless theuniversity has a rule that two instructors cannot have the same name,which is a rather unlikey scenario).2.5What is the result offirst performing the cross product of student andadvisor,and then performing a selection operation on the result with thepredicate s id=ID?(Using the symbolic notation of relational algebra,this query can be written ass id=I D(student×advisor).)Answer:The result attributes include all attribute values of studentfollowed by all attributes of advisor.The tuples in the result are asfollows.For each student who has an advisor,the result has a rowcontaining that students attributes,followed by an s id attribute identicalto the students ID attribute,followed by the i id attribute containing theID of the students advisor.Students who do not have an advisor will not appear in the result.Astudent who has more than one advisor will appear a correspondingnumber of times in the result.2.6Consider the following expressions,which use the result of a relationalalgebra operation as the input to another operation.For each expression,explain in words what the expression does.a.year≥2009(takes)1studentb.year≥2009(takes1student)c. ID,name,course id(student1takes)Answer:a.For each student who takes at least one course in2009,displaythe students information along with the information about whatcourses the student took.The attributes in the result are:ID,name,dept name,tot cred,course id,section id,semester,year,gradeb.Same as(a);selection can be done before the join operation.c.Provide a list of consisting ofExercises3ID,name,course idof all students who took any course in the university.2.7Consider the relational database of Figure??.Give an expression in therelational algebra to express each of the following queries:a.Find the names of all employees who live in city“Miami”.b.Find the names of all employees whose salary is greater than$100,000.c.Find the names of all employees who live in“Miami”and whosesalary is greater than$100,000.Answer:a. name(city=“Miami”(employee))b. name(salary>100000(employee))c. name(city=“Miami”∧salary>100000(employee))2.8Consider the bank database of Figure??.Give an expression in therelational algebra for each of the following queries.a.Find the names of all branches located in“Chicago”.b.Find the names of all borrowers who have a loan in branch“Down-town”.Answer:a. branch name(branch city=“Chicago”(branch))b. customer name(branch name=“Downtown”(borro w er1loan))。
数据库系统概论第六版教学大纲
数据库系统概论第六版的教学大纲主要包括以下几个部分：
1. 数据库系统的基本概念：介绍数据库系统的定义、发展历程和基本原理，以及数据库系统的组成和体系结构。
2. 数据模型：介绍常见的数据模型，如层次模型、网状模型和关系模型，以及面向对象数据模型和XML数据模型等。
3. 数据库设计和规范化：介绍数据库设计的基本概念和原理，以及数据库设计和规范化的方法和步骤，包括需求分析、概念设计、逻辑设计、物理设计等。
4. 数据库管理系统：介绍数据库管理系统的基本功能和组成，以及数据库管理系统的工作原理和实现技术，包括存储管理、事务管理、并发控制、数据备份和恢复等。
5. 数据库系统应用：介绍数据库系统的应用领域和案例，包括企业级应用、电子商务、电子政务、物联网等。
6. 数据库系统性能优化：介绍数据库系统性能优化的方法和技巧，包括查询优化、索引优化、系统配置优化等。
7. 数据库安全和隐私保护：介绍数据库安全和隐私保护的基本概念和原理，以及数据库安全和隐私保护的技术和方法，包括访问控制、加密技术、审计等。
8. 数据库系统的未来发展：介绍数据库系统的未来发展趋势和方向，包括大数据处理、云计算、人工智能等。
以上是数据库系统概论第六版教学大纲的简要介绍，具体的教学内容和教学方法可以根据实际情况进行调整和补充。
2Chapter11Indexing and Hashingb.c.11.4Answer:•With structure11.3.a:Insert9:10:InsertExercises3Delete23:Delete19:•With structure11.3.b:Insert9:Insert4Chapter 11Indexing and HashingDelete23:Delete 19:•With structure 11.3.c:Insert9:Insert10:Insert8:Delete 23:Delete 19:Exercises511.5Answer:If there are K search-key values and m −1siblings are involvedin the redistribution,the expected height of the tree is:log ⌊(m −1)n /m ⌋(K )11.6Answer:Extendable hash structure000 001010 011 100101 110 11111.7Answer:a.Delete 11:From the answer to Exercise 11.6,change the third bucketto:At this stage,it is possible to coalesce the second and third buckets.Then it is enough if the bucket address table has just four entriesinstead of eight.For the purpose of this answer,we do not do the coalescing.b.Delete 31:From the answer to 11.6,change the last bucket to:6Chapter11Indexing and Hashingc.Insert1:From the answer to11.6,change thefirst bucket to:d.Insert15:From the answer to11.6,change the last bucket to:11.8Answer:The pseudocode is shown in Figure11.1.11.9Answer:Let i denote the number of bits of the hash value used in thehash table.Let bsize denote the maximum capacity of each bucket.Thepseudocode is shown in Figure11.2.Note that we can only merge two buckets at a time.The common hashprefix of the resultant bucket will have length one less than the two bucketsmerged.Hence we look at the buddy bucket of bucket j differing from itonly at the last bit.If the common hash prefix of this bucket is not i j,thenthis implies that the buddy bucket has been further split and merge is notpossible.When merge is successful,further merging may be possible,which is handled by a recursive call to coalesce at the end of the function.11.10Answer:If the hash table is currently using i bits of the hash value,thenmaintain a count of buckets for which the length of common hash prefixis exactly i.Consider a bucket j with length of common hash prefix i j.If the bucketis being split,and i j is equal to i,then reset the count to1.If the bucketis being split and i j is one less that i,then increase the count by1.It thebucket if being coalesced,and i j is equal to i then decrease the count by1.If the count becomes0,then the bucket address table can be reduced insize at that point.However,note that if the bucket address table is not reduced at that point,then the count has no significance afterwards.If we want to postpone thereduction,we have to keep an array of counts,i.e.a count for each value ofExercises7 functionfindIterator(value V){/*Returns an iterator for the search on the value V*/Iterator iter();Set iter.v alue=V;Set C=root nodewhile(C is not a leaf node)beginLet i=samllest number such that V<=C.K iif there is no such number i then beginLet P m=last non-null pointer in the nodeSet C=C.P m;endelse Set C=C.P i;end/*C is a leaf node*/Let i be the least value such that K i=Vif there is such a value i then beginSet iter.index=i;Set iter.page=C;Set iter.acti v e=T RUE;endelse if(V is the greater than the largest value in the leaf)then beginif(C.P n.K1=V)then beginSet iter.page=C.P n;Set iter.index=1;Set iter.acti v e=T RUE;endelse Set iter.acti v e=F AL SE;endelse Set iter.acti v e=F AL SE;return(iter)}Class Iterator{variables:value V/*The value on which the index is searched*/boolean active/*Stores the current state of the iterator(TRUE or FALSE)*/int index/*Index of the next matching entry(if active is TRUE)*/PageID page/*Page Number of the next matching entry(if active is TRUE)*/ function next(){if(active)then beginSet ret Page=page;Set retI ndex=index;if(index+1=page.size)then beginpage=page.P nindex=0endelse index=index+1;if(page.K index=V)then acti v e=F AL SE;return(ret Page,retI ndex)endelse return null;}}Figure11.1Pseudocode forfindIterator and the Iterator class8Chapter11Indexing and Hashingdelete(value K l)beginj=first i high-order bits of h(K l);delete value K l from bucket j;coalesce(bucket j);endcoalesce(bucket j)begini j=bits used in bucket j;k=any bucket withfirst(i j−1)bits same as thatof bucket j while the bit i j is reversed;i k=bits used in bucket k;if(i j=i k)return;/*buckets cannot be merged*/if(entries in j+entries in k>bsize)return;/*buckets cannot be merged*/move entries of bucket k into bucket j;decrease the value of i j by1;make all the bucket-address-table entries,which pointed to bucket k,point to j;coalesce(bucket j);endFigure11.2Pseudocode for deletioncommon hash prefix.The array has to be updated in a similar fashion.Thebucket address table can be reduced if the i th entry of the array is0,wherei is the number of bits the table is using.Since bucket table reduction isan expensive operation,it is not always advisable to reduce the table.Itshould be reduced only when sufficient number of entries at the end ofcount array become0.11.11Answer:We reproduce the instructor relation below.Exercises9 ID dept salary10101Comp.Sci.Wu9000015151MusicEinstein9500032343HistoryGold8700045565Comp.Sci.Califieri6200076543FinanceCrick7200083821Comp.Sci.Kim80000a.Bitmap for salary,with S1,S2,S3and S4representing the given inter-vals in the same orderS1000000000000S3010*********b.The question is a bit trivial if there is no bitmap on the deptname attribute is:Comp.Sci010********* Music000101000000 History000000000100 Elec.Eng.010********* Finance010*********10Chapter11Indexing and HashingScan on these records with salary80000or more gives Wu and Singhas the instructors who satisfy the given query.11.12Answer:If the index entries are inserted in ascending order,the newentries get directed to the last leaf node.When this leaf node getsfilled,it is split into two.Of the two nodes generated by the split,the left nodeis left untouched and the insertions takes place on the right node.Thismakes the occupancy of the leaf nodes to about50percent,except the lastleaf.If keys that are inserted are sorted in descending order,the above situationwould still occur,but symmetrically,with the right node of a split nevergetting touched again,and occupancy would again be50percent for allnodes other than thefirst leaf.11.13Answer:a.The cost to locate the page number of the required leaf page foran insertion is negligible since the non-leaf nodes are in memory.On the leaf level it takes one random disk access to read and onerandom disk access to update it along with the cost to write onepage.Insertions which lead to splitting of leaf nodes require anadditional page write.Hence to build a B+-tree with n r entries ittakes a maximum of2∗n r random disk accesses and n r+2∗(n r/f)page writes.The second part of the cost comes from the fact that inthe worst case each leaf is halffilled,so the number of splits thatoccur is twice n r/f.The above formula ignores the cost of writing non-leaf nodes,sincewe assume they are in memory,but in reality they would also bewritten eventually.This cost is closely approximated by2∗(n r/f)/f,which is the number of internal nodes just above the leaf;we canadd further terms to account for higher levels of nodes,but these aremuch smaller than the number of leaves and can be ignored.b.Substituting the values in the above formula and neglecting the costfor page writes,it takes about10,000,000∗20milliseconds,or56hours,since each insertion costs20milliseconds.Exercises11c.function insert leaf(value K,pointer P)if(tree is empty)create an empty leaf node L,which is also the rootelse Find the last leaf node in the leaf nodes chain Lif(L has less than n−1key values)then insert(K,P)at thefirst available location in Lelse beginCreate leaf node L1Set L.P n=L1;Set K1=last value from page Linsert parent(1,L,K1,L1)insert(K,P)at thefirst location in L1endfunction insert parent(level l,pointer P,value K,pointer P1)if(level l is empty)then beginCreate an empty non-leaf node N,which is also the rootinsert(P,K,P1)at the starting of the node Nreturnelse beginFind the right most node N at level lif(N has less than n pointers)then insert(K,P1)at thefirst available location in Nelse beginCreate a new non-leaf page N1insert(P1)at the starting of the node Ninsert parent(l+1,pointer N,value K,pointer N1)endendThe insert leaf function is called for each of the value,pointerpairs in ascending order.Similar function can also be build for de-scending order.The search for the last leaf or non-leaf node at anylevel can be avoided by storing the current last page details in anarray.The last node in each level might be less than halffilled.To makethis index structure meet the requirements of a B+-tree,we can re-distribute the keys of the last two pages at each level.Since the lastbut one node is always full,redistribution makes sure that both ofthen are at least halffilled.11.14Answer:In a B+-tree index orfile organization,leaf nodes that areadjacent to each other in the tree may be located at different places ondisk.When afile organization is newly created on a set of records,it ispossible to allocate blocks that are mostly contiguous on disk to leafsnodes that are contiguous in the tree.As insertions and deletions occur12Chapter11Indexing and Hashingon the tree,sequentiality is increasingly lost,and sequential access has towait for disk seeks increasingly often.a.One way to solve this problem is to rebuild the index to restoresequentiality.b.i.In the worst case each n-block unit and each node of the B+-treeis halffilled.This gives the worst case occupancy as25percent.ii.No.While splitting the n-block unit thefirst n/2leaf pages areplaced in one n-block unit,and the remaining in the second n-block unit.That is,every n-block split maintains the order.Hence,the nodes in the n-block units are consecutive.iii.In the regular B+-tree construction,the leaf pages might not besequential and hence in the worst case,it takes one seek per leafing the block at a time method,for each n-node block,we will have at least n/2leaf nodes in it.Each n-node block canbe read using one seek.Hence the worst case seeks comes downby a factor of n/2.iv.Allowing redistribution among the nodes of the same block,doesnot require additional seeks,where as,in regular B+-tree werequire as many seeks as the number of leaf pages involvedin the redistribution.This makes redistribution for leaf blocksefficient with this scheme.Also the worst case occupancy comesback to nearly50percent.(Splitting of leaf nodes is preferredwhen the participating leaf nodes are nearly full.Hence nearly50percent instead of exact50percent)。
C H A P T E R25Advanced Data Types and New ApplicationsPractice Exercises25.1What are the two types of time,and how are they different?Why doesit make sense to have both types of time associated with a tuple?Answer:A temporal database models the changing states of someaspects of the real world.The time intervals related to the data storedin a temporal database may be of two types-valid time and transactiontime.The valid time for a fact is the set of intervals during which the factis true in the real world.The transaction time for a data object is the set oftime intervals during which this object is part of the physical database.Only the transaction time is system dependent and is generated by thedatabase system.Suppose we consider our sample bank database to be bitemporal.Only the concept of valid time allows the system to answer queries suchas-“What was Smith’s balance two days ago?”.On the other hand,queries such as-“What did we record as Smith’s balance two daysago?”can be answered based on the transaction time.The differencebetween the two times is important.For example,suppose,three daysago the teller made a mistake in entering Smith’s balance and correctedthe error only yesterday.This error means that there is a differencebetween the results of the two queries(if both of them are executedtoday).25.2Suppose you have a relation containing the x,y coordinates and namesof restaurants.Suppose also that the only queries that will be askedare of the following form:The query specifies a point,and asks if thereis a restaurant exactly at that point.Which type of index would bepreferable,R-tree or B-tree?Why?Answer:The given query is not a range query,since it requires onlysearching for a point.This query can be efficiently answered by a B-treeindex on the pair of attributes(x,y).12Chapter25Advanced Data Types and New Applications25.3Suppose you have a spatial database that supports region queries(withcircular regions)but not nearest-neighbor queries.Describe an algo-rithm tofind the nearest neighbor by making use of multiple regionqueries.Answer:Suppose that we want to search for the nearest neighbor of apoint P in a database of points in the plane.The idea is to issue multipleregion queries centered at P.Each region query covers a larger area ofpoints than the previous query.The procedure stops when the result ofa region query is non-empty.The distance from P to each point withinthis region is calculated and the set of points at the smallest distance isreported.25.4Suppose you want to store line segments in an R-tree.If a line segment isnot parallel to the axes,the bounding box for it can be large,containinga large empty area.•Describe the effect on performance of having large bounding boxes on queries that ask for line segments intersecting a given region.•Briefly describe a technique to improve performance for such queries and give an example of its benefit.Hint:You can divide segmentsinto smaller pieces.Answer:Large bounding boxes tend to overlap even where the regionof overlap does not contain any information.The followingfigure:Rshows a region R within which we have to locate a segment.Notethat even though none of the four segments lies in R,due to the largebounding boxes,we have to check each of the four bounding boxes toconfirm this.A significant improvement is observed in the follwoingfigure:Practice Exercises3Rwhere each segment is split into multiple pieces,each with its own bounding box.In the second case,the box R is not part of the boxes indexed by the R-tree.In general,dividing a segment into smaller pieces causes the bounding boxes to be smaller and less wasteful of area. 25.5Give a recursive procedure to efficiently compute the spatial join of tworelations with R-tree indices.(Hint:Use bounding boxes to check if leaf entries under a pair of internal nodes may intersect.)Answer:Following is a recursive procedure for computing spatial join of two R-trees.SpJoin(node n1,node n2)beginif(the bounding boxes of n1and n2do not intersect)return;if(both n1and n2are leaves)output all pairs of entries(e1,e2)such thate1∈n1and e2∈n2,and e1and e2overlap;if(n1is not a leaf)NS1=set of children of n1;elseNS1={n1};if(n1is not a leaf)NS1=set of children of n1;elseNS1={n1};for each ns1in NS1and ns2in NS2;SpJoin(ns1,ns2);end25.6Describe how the ideas behind the RAID organization(Section10.3)canbe used in a broadcast-data environment,where there may occasionally be noise that prevents reception of part of the data being transmitted.4Chapter25Advanced Data Types and New ApplicationsAnswer:The concepts of RAID can be used to improve reliability ofthe broadcast of data over wireless systems.Each block of data that isto be broadcast is split into units of equal size.A checksum value iscalculated for each unit and appended to the unit.Now,parity data forthese units is calculated.A checksum for the parity data is appendedto it to form a parity unit.Both the data units and the parity unit arethen broadcast one after the other as a single transmission.On reception of the broadcast,the receiver uses the checksums to verify whether each unit is received without error.If one unit is foundto be in error,it can be reconstructed from the other units.The size of a unit must be chosen carefully.Small units not only requiremore checksums to be computed,but the chance that a burst of noisecorrupts more than one unit is also higher.The problem with usinglarge units is that the probability of noise affecting a unit increases;thus there is a tradeoff to be made.25.7Define a model of repeatedly broadcast data in which the broadcastmedium is modeled as a virtual disk.Describe how access time anddata-transfer rate for this virtual disk differ from the correspondingvalues for a typical hard disk.Answer:We can distinguish two models of broadcast data.In the caseof a pure broadcast medium,where the receiver cannot communicatewith the broadcaster,the broadcaster transmits data with periodic cy-cles of retransmission of the entire data,so that new receivers can catchup with all the broadcast information.Thus,the data is broadcast in acontinuous cycle.This period of the cycle can be considered akin to theworst case rotational latency in a disk drive.There is no concept of seektime here.The value for the cycle latency depends on the application,but is likely to be at least of the order of seconds,which is much higherthan the latency in a disk drive.In an alternative model,the receiver can send requests back to the broadcaster.In this model,we can also add an equivalent of disk accesslatency,between the receiver sending a request,and the broadcasterreceiving the request and responding to it.The latency is a function ofthe volume of requests and the bandwidth of the broadcast medium.Further,queries may get satisfied without even sending a request,sincethe broadcaster happened to send the data either in a cycle or basedon some other receivers request.Regardless,latency is likely to be atleast of the order of seconds,again much higher than the correspondingvalues for a hard disk.A typical hard disk can transfer data at the rate of1to5megabytes persecond.In contrast,the bandwidth of a broadcast channel is typicallyonly a few kilobytes per second.Total latency is likely to be of the orderof seconds to hundreds or even thousands of seconds,compared to afew milliseconds for a hard disk.25.8Consider a database of documents in which all documents are keptin a central database.Copies of some documents are kept on mobilePractice Exercises5 computers.Suppose that mobile computer A updates a copy of docu-ment1while it is disconnected,and,at the same time,mobile computer B updates a copy of document2while it is disconnected.Show how the version-vector scheme can ensure proper updating of the central database and mobile computers when a mobile computer reconnects. Answer:Let C be the computer onto which the central database is loaded.Each mobile computer(host)i stores,with its copy of each document d,a version-vector–that is a set of version numbers V d,i,j, with one entry for each other host j that stores a copy of the document d,which it could possibly update.Host A updates document1while it is disconnected from C.Thus, according to the version vector scheme,the version number V1,A,A is incremented by one.Now,suppose host A re-connects to C.This pair exchanges version-vectors andfinds that the version number V1,A,A is greater than V1,C,A by1,(assuming that the copy of document1stored host A was updated most recently only by host A).Following the version-vector scheme, the version of document1at C is updated and the change is reflected by an increment in the version number V1,C,A.Note that these are the only changes made by either host.Similarly,when host B connects to host C,they exchange version-vectors,and host Bfinds that V1,B,A is one less than V1,C,A.Thus,the version number V1,B,A is incremented by one,and the copy of docu-ment1at host B is updated.Thus,we see that the version-vector scheme ensures proper updating of the central database for the case just considered.This argument can be very easily generalized for the case where multiple off-line updates are made to copies of document1at host A as well as host B and host C.The argument for off-line updates to document2is similar.。
