Text-based Decision Making with Artificial Immune Systems
HANA KOPACKOVA, LUDEK KOPACEK
Faculty of Economics and Administration, Institute of System Engineering and Informatics
University of Pardubice
Studentska 84, Pardubice, 53210
CZECH REPUBLIC
hana.kopackova@upce.cz, ludek.kopacek@upce.cz
Abstract: Decision making is one of the most important manager activities. Especially in these days, full of different information, it is necessary to distinguish between important and unimportant information to make significant decision. Especially unstructured documents can be full of hidden information. Usage of text classification can significantly lower manager workload and raise objectivity of decision making process. Automated processing of text documents can also prevent simplification and generalisation, which allow us to decide on the base of small amount of cases and widen this decision on all cases. The aim of this paper is to find methods which fulfil criteria put on text classification done by managers due decision making process.
Key-Words: -Decision making, Management, Text classification, Artificial immune system, Immunos-81
1 Introduction
Proces of decision making represents complex and difficult problem, which is highely dependant on the accessible information. Seeing that the amount of produced information is growing faster than the ability of information consumers to find, retrieve and use this information, decision makers must solve this situation. It is quite easy to find many methods that makes access to numerical data. For example database software, spreadsheets or statistic packages. But it is much harder to provide effective access to textual data that consists of expresions in human language, because of the relationship between its form and its content, which is less clear than in numeric data.
2 Proces of decision making
We can find three main types of decision making: intuitive, rational and limited rationality. Intuitive decision making is used when fast solution is needed. The act of decision-making is done by individual, mostly using intuition and previous experience. As an advantages of intuitive decision making we can find speed and interest of decision maker. Nevertheless emotion based intuitive decision-making can also have some serious disadvantages. Failure to consider other available options, inaccurate or irrelevant information used for decision and finally, intuitive reasoning is problematic in group situations where decisions need to be made collectively.
Rational decision making model assumes that decision maker posses a utility function (an ordering by preference among all the possible outcomes of choice), that all the alternatives among which choice could be made were known, and that the consequences of choosing each alternative could be ascertained [1], [2]. Although the assumptions of rational model (results and alternatives are ascertained, information are complete and so on) cannot be satisfied even remotely for most complex situations in the real world, they may be satisfied approximately in some isolated problem situations without uncertainty.
Although examples of rational decision-making process are not rare, limits of incomplete information, inconsistency, and institutional constraints on alternatives brings us to the models of limited (bounded) rationality. These models retain the same process of decision-making but incorporate many additional limitations. Simon [3] describes ‘satisfying’ behavior - a decision making process which searches for ‘good enough’ options, rather than an optimum solution. With satisfying decision making becomes something which is carried out in a limited time, with limited information and with some limits on the individuals concerned.
3 Textual information in decision making
Problem of textual information is considered by Mark Tucker in [4]. ”The ratio of unstructured to structured information in most organizations is easily 9 to 1, yet many of us spent most of our time worrying about – indeed, dedicating our careers to – managing the most familiar 10 percent of the problem: structured information… Business processes have always relied on unstructured information, and the volume and sources
of this information are increasing, not decreasing. The World Wide Web, the corporate Intranet, email, and online discussion groups are just a few of the familiar examples… Information drives our business decisions,
and it always has. What has changed dramatically is the kind of decisions we make. As the economy shifts from an industrial model to a knowledge-driven one, we need more and more information to support the decision-making process, and the dynamic nature of our business environment is such that less and less of this information fits the structured information model.” Forest Research [5] has predicted that unstructured data (such as text) will become the predominant data type stored online. This implies a great opportunity of possible more effective use of repositories of business communications, and other unstructured data, by using computer analysis. But the problem with text is that it is not designed for using by computers. Unlike the tabular information typically stored in databases today, documents have only limited internal structure if any. Furthermore, the important information they contain is not explicit but is implicit: buried in the text. [6] Managerial decision-making process can be highly dependent upon hidden information in text documents nevertheless careful reading and sorting of documents is time consuming work. This type of activity waste working time of managers and at the end, it can cause wrong decision.
Now we must ask how we can help managers to cope with numerous text documents.
The aim of this paper is finding of methods which can fasten decision-making process on all levels of management and make it much easier for managers using great amount of information in text form. Usage of text categorization methods, which are well known in the branch of information retrieval, but they are not often used to support decision making process can significantly lower manager workload. Another aim can be defined as raise of objectivity in decision-making process. Automated processing of text documents can prevent simplifications and generalisation, which allow us to decide on the base of small amount of cases and widen this decision on all cases. Unfortunately, this approach is commonly used having too much text documents and only little time to read them.
4 Text processing methods
There are two types of text processing methods.
Firs can be called linguistic or text understanding and cover natural language processing. People engaged in this kind of research try to design and build software that will analyze, understand, and generate languages that humans use naturally. "Understanding" language means, among other things, knowing what concepts a word or phrase stands for and knowing how to link those concepts together in a meaningful way. This type of research is not the one we chosen for problem solution. While linguistic methods are very difficult so that tools are still not done, statistical methods proved in data mining can find word patterns in large collections of digital documents just now.
Second type can be named as statistical or text classification even if it covers also methods from machine learning. These text mining methods are similar to classical data mining methods, only with transformation of text to standard numerical form. Those methods proved successful without understanding specific properties of text such as the concepts of grammar or the meaning of words. Strictly low-level frequency information is used, such as the number of times a word appears in a document and then methods of machine learning are applied. This approach will be taken as a basis for this paper.
5 Text classification
The term text classification cover number of information retrieval tasks that are widely accepted as distinct, but which all involve grouping of textual entities. This entities (documents) can be grouped according to human classifier or some learning algorithm, whose job it is to take training examples and create classifier, which is then able to look at further examples and decide if they fit into the learned concept or not.
Here are some examples of possible usage of text classification; building of personalised Netnews filter which learns about the news-reading preferences of a user [7], classification of news stories [8] or guidance of a user‘s search on the Web [9, 10, 11, 12].
A growing number of learning algorithms have been applied to text classification task, such as Naive Bayesian [13], Bayesian Network [14], Decision Tree [15] [16], Neural Network [17], Linear Regression [18], k-NN [19], Support Vector Machines [20, 21], Boosting [22] and Genetic Algorithms [23]. A comprehensive comparative evaluation of a wide-range of text classification methods is reported in [20, 21].
5.1 Assumptions for application of text classification in decision making process
Learning algorithms for text classification were usually tested in specific environment which cover:
?great amount of training documents (Reuters collection, newsgroups, TDT Pilot study
corpus... – about 20000 documents),
?and approximately similar length of documents.
These conditions are almost impossible to guarantee in real managerial decision-making. The aim of this paper and the whole research is to find such methods, which can be applied directly by managers if they need them. This statement assumes that managers are the persons who will decide which documents will be used as
training documents. No one can expect that he/she will have great number of training documents with similar length. Probably they will use maximum tens of documents.
Methods suitable to support managerial decision-making process must fulfil these criteria: ?easy to implement,
?fast to process categorization,
?cheap,
?stable for differences in length of document,
?learning model build from small number of documents,
?high precision.
Two classical methods and one very new method will
be tested for being used in managerial decision-making. These methods are K-nearest neighbour, Naive Bayes algorithm and Artificial Immune System (AIS) classifier. Forasmuch as classical methods are well known, we will describe only principles of AIS classifier.
6 Artificial Immune System classifier
The complexity of many computational problems has led to the development of a range of innovative techniques. One area of research, which has attracted a large amount of interest in recent years, is evolutionary computing. The central idea is the evolution of population of candidate solutions through the application of operators inspired by natural selection and random variation. The humane immune system is an example of a system, which maintains a population of diverse individuals, and has provided the inspiration for
a number of artificial evolutionary systems.
Mainstream of thoughts about artificial immune systems concerns on three aspects; immune network, clonal selection, and negative selection. Immune network theory [24, 25] proposes that the immune system maintains a network of cells that learn and maintain memory using feedback mechanism. This theory says that even thou information is learned, it can be forgotten
if the information is not intensified. Clonal selection theory [26, 27] is the idea that those cells that are effective at recognising pathogenic material are selected
to survive and propagate. Negative selection theory [28, 29] is based on eliminating those cells whose receptors are capable of recognizing self-antigens. This process can be used for anomaly detection algorithms.
The human immune system as a whole consists of a multilayered architecture presenting different types of defence against infectious material called pathogens. The most important layer that inspired birth of artificial immune systems is the third layer – the cellular layer. This layer is composed of variety of different cell types with different roles. Most of the cells belong to the leukocyte family, usually known as white blood cells. These cells (especially B-cells and T-cells) are responsible for anomaly detection, which is proceeded according to affinity (degree of similarity between a recognition cell and an antigen). The adaptive ability of the immune system is a process called affinity maturation. During an immune response the recognition cell generate many clones of itself in an attempt to gain
a better match next time when the antigen is seen (the process is called clonal expansion). Each clone is then mutated in proportion to the affinity between the recognition cell and the antigen (somatic hypermutation). The last step is called clonal selection and cover elimination of newly differentiated clones carrying low affinity antigenic receptors.
According to Forrest et al. [30] and Twycross [31] some general properties of the immune system can be defined. These properties are diversity, distributed and dynamic nature, error tolerance, self-protection and adaptability. Diversity means that different people are susceptible to different pathogens. Dynamic and distributed nature is given by the number of individual components that are constantly being created and destroyed effecting independently anywhere in the body without central coordination. Error tolerance is based on the fact that the humane immnue system makes very few mistakes. The same system which protect the body also protect the immune system itself, due to this fact we can talk about self-protection. Adaptability represents basic presumption for creation of artificial immune systems. The ability to identify and respond to novel pathogens and also retain a memory of past infections started AIS research.
6.1 Immunos-81
The first attempt to use immune system principles as a basis for supervised learning and classification system
by Carter [32] was called Immunos-81. The system was designed with the intent of taking advantages of the features of immune systems without remaining too close
to the biological aspects. Carter made the argument that the majority of AIS reviewed at the time adhere too closely to the biological metaphor, which although provides some useful architectural algorithm elements may not necessary be useful from a computational point
of view.
The immune system concepts are reduced to their most fundamental level before they are incorporated into the prototype. For example, B cells/antibodies are not randomly generated with a range of binding affinities. Instead, B-cell/antibody generation is under the control solely of entered data.
The primary reasons why the immune system was selected as the basis for a classification algorithm was
given its ability to readily accept patterns of arbitrary length, and its ability to maintain and exploit previously learned information efficiently for improved performance in future encounters. It was shown to provide good results in terms of classification accuracy on the Cleveland heart disease datasets.
The terminology used by Carter and explained by Brownlee [33] is described in next section.
T-cell – These elements learn the primary structure of antigens in the system and decide what is interesting to the system. The T-cells act as gatekeepers to the system, both partitioning learned information and deciding how new information is exposed to the system. One T-cell exists for each antigen-type in the system, and each T-cell has one or more clone (groups) of B-cells.
B-cell – These elements learn the surface features of antigens. In the system, B-cells represent an instance of an antigen-type, more specifically an instance of an antigengroup. It is mentioned by Carter that B-cells are not represented explicitly by Immunos- 81, rather they are incorporated into a clone or group structure.
Antigen – An antigen is a molecule that elicits an immune response, which in this case is represented by a single training data instance. An antigen is represented as a data vector of attribute or field values where the nature of each attribute (name, data type) are known. Antigens can be variable length, and can have missing field values. Further, Immunos-81 supports the acceptance of antigens from differing problem domains. Antigen-type – Confusingly called antigen-class by Carter, the antigen-type refers to a specific problem domain identified by a specific name and series of attributes. This is the primary structure learned by T-cells.
Antigen-group (clone) – An antigen group is a set of antigens of a specific antigen-type that are of interest. Given that the nature of the problem addressed by Immunos-81 is that of classification, an antigen-group represents all antigens with a specific classification label. Represented in the system, this group is called a clone of B-cells controlled by a single T-cell. Concentration (antigenicity) – An interesting term used
to describe the number of antigens (training instances) with a specific class label (an antigen-group) was antigen concentration or antigenicity. This concentration obviously indicates the amount of attention each antigen-group will receive, as well as aids in defining the size of the antigen-groups clone of B-cells.
Paratope and Epitope – A paratope is a part of the receptor on an antibody molecule that binds to a matching part of the antigen molecule called the epitope.
In Immunos-81 these terms are used to described individual attributes on the recognition cells (T-cells and
B-cells), and antigens respectively. Affinity – A recognition cell has specificity with an antigen, the degree that is called affinity. In the Immunos-81 system, affinity is a match between a similarity/dissimilarity measure between a T-cell and an antigen, and a B-cell and an antigen.
Avidity – Avidity is the term used to describe the sum affinities for all B-cells of a clone for a given antigen. This value is used to allow clones of a T-cell to compete with each other to classify an antigen.
Amino Acid Library (AALib) – As mentioned, antigens that exhibit the largest immune response consists of proteins, which are sequences of amino acids. Immunos-81 uses a similar concept to manage antigen details. A given-antigen type has a name (problem domain name) and a series of attributes (order, attribute names, data types, etc.). This information is required for a T-cell and throughout the learning process. All this domain knowledge is centralised and stored in an AALib construct.
7 Experimental results
In the testing environment we used 50 documents in Czech language; 25 of them were focused directly on the branch of waste management and the rest 25 documents were not specialised – only covered problem of environment. The shortest document had only 98 words and the longest had 1400 words. For feature selection were used five different methods: Term frequency selection (selected were features that occurred in the document more then once), Document frequency (selected were features that occurred in more then one document), Chi-square, Mutual information, and Information gain [34]. Two methods for term weighting were tested TF – term frequency and TFIDF. After pre-processing stage database was filled with 10571 words.
The result can be seen in figure 1 (CCI means correctly classified instances and K means Kappa statistics). Experiments that were published for example in [19] introduced K-NN method as very efficient, however these experiments used Reuters corpus filled with articles of similar legth. Our experiments proved that K nearest neighbour algorithm is very sensitive to length differences (documents using same words have long Euclidean distance between them if they differ in length) so it is not suitable to support managerial decision-making as it was described here. On the other hand Naive Bayes and Artificial Immune Systems can serve as very helpful tool.
Differences between TF and TFIDF methods are not so dominant to say that one of them is better but it can be said that TF method is cheaper and sufficing.
Naive Bayes K-NN Imunos-81
CCI-NB [%] K-NB [%] CCI-KNN [%]K-KNN [%]CCI-81 [%] K-81 [%] TF-TF 88,00 76,0056,0012,00100,00100,00
TF-DF 90,00 80,0056,0012,00100,00100,00 TFchi-square 96,00 92,0062,0024,00100,00100,00 TFinformation gain 98,00 96,0062,0024,00100,00100,00 TFmutual information 96,00 92,0050,000,00100,00100,00 TFIDF – TF 90,00 80,0056,0012,0096,0092,00
TFIDF-DF 92,00 84,0058,0016,0098,0096,00 TFIDFchi-square 96,00 92,0066,0032,00100,00100,00 TFIDFinformation gain 96,00 92,0068,0036,00100,00100,00 TFIDFmutual information 92,00 84,0050,000,0096,0092,00 Figure 1: Experimental results
8 Conclusion
Unstructured information in the form of textual documents are used in managerial-decision making very often, nevertheless support for this kind of action is inadequate. In this paper we shortly introduce tools that deal with textual information including proposal of methods that fulfil demands specially put on this kind of decision-making.
9 Acknowledgement
This research and paper was created with a kind support
of the Grant Agency of the Czech Republic, grant number GACR 402/05/P155.
References:
[1] H. A Simon, A Behavioral Model of Rational
Choice. Cowle: Foundation Paper 98. The Quarterly Journal of Economics, vol. LXIX, February 1955. [2] H. A Simon, Report of the Research Briefing Panel
on Decision Making and Problem Solving. National Academy of Sciences. Washington, DC: National Academy Press, 1986
[3] H. A. Simon, Models of Bounded Rationality.,
Cambridge, M.A.: Harper and Row, 1983.
[4] Tucker, M. Dark Matter of Decision Making.
Intelligent Enterprise Magazine, vol. 2, num. 13. 1999 [5] Forrester Research. Coping with Complex Data. The
Forrester Report, 1995
[6] Tkach, D. Text Mining Technology, Turning
Information Into Knowledge. White paper from IBM.
1998
[7] Lang K., NewsWeeder: Learning to Filter Netnews,
International Conference on Machine Learning,
1995.
[8] Hayes P. et al., A news story categorization system,
Second Conference on Applied Natural Language Processing, 1988 [9] Mitchell T. et al., WebWatcher: A Learning
Apprentice for the World Wide Web, AAAI Sprig
Symposium on Information Gathering from Heterogenous, Distributed Environments, 1995
[10]Bollacker K., Lawrec S., Giles L., Citeseer: An
Autonomous System for Processing and Organizing
Scientific Literature on the Web, Conference on Automated Learning and Discovery, Pittsburgh, 1998 [11] Mladenic D.: Personal WebWatcher:
Implementation and Design, Tech. Report IJS-DP-
7472, J. Stefan Inst., 1996
[12] Sklenák V. a kol., Data, informace, znalosti a
internet, 1. vyd. Praha, C. H. Beck, 2001
[13] Joachims T., A probabilistic analysis of the
Rocchio algorithm with TFIDF for text categorization, Proceedings of ICML-97, 14th
International Conference on Machine Learning,
1997.
[14] Sahami M., Learning limited dependence Bayesian
classifier. In KDD- 96: Proceedings of the second
international Conference on Knowledge Discovery
and Data Mining, AAAI press, 335-338, 1996.
[15] Quinlan J. R., C4.5: Programs for machine
learning, Morgan Kaufmann, 1993.
[16] Weiss S. M et. al., Maximizing text-mining
performance, IEEE Intelligent systems,1999.
[17] Wiener E., Pederson J. O., Weigend A. S., A neural
network approach to topic spotting. Proceedings of
SDAIR-95, 4th Annual Symposium on Document Analysis and Information Retrieval, 1995.
[18] Yang Y., Chute C. G., A linear least squares fit
mapping method for information retrieval from natural language texts. Proceedings of the 14th
International Conference on Computational Linguistics (COLING 92), 1992.
[19] Yang Y., An evaluation of statistical approaches to
text categorization. Journal of Information Retrieval,
Vol.1, No.1/2, 1999.
[20] Dumais S. et. al., Inductive learning algorithms and
representations for text categorization. Proceedings
of the 7th International Conference on Information and Knowledge Management (CIKM 98), 1998. [21] Joachims T., Text categorization with support
vector machines: learning with many relevant features. Proceedings of ECML-98, 10th European Conference on Machine Learning, 1998.
[22] Schapire R. E., Singer Y., Boostexter: A boosting-
based system for text categorization. Machine Learning, 39(2/3), 2000.
[23] Prasanna K., Khemani D. Applying Set Covering
Problem in Instance Set Reduction for Machine Learning Algorithms. WSEAS Multiconference: Software Engineering, Parallel & Distributed Systems (SEPADS 2004). Salzburg, Austria, 2004. [24] Perelson, A. S. Immune Network Theory. Immunol.
Rev., 110 (1989) 5-36.
[25] Jerne, N. K. Towards a Network theory of the
Immune System. Annals of Immunology, 125c:373–
389, 1974.
[26] Burnet, F. M. The Clonal Selection Theory of
Immunity. Vanderbilt University Press, Nashville, TN, 1959.
[27] de Castro, L. N. - Von Zuben, F. J., "The Clonal
Selection Algorithm with Engineering Applications", In Proceedings of GECCO'00, Workshop on Artificial Immune Systems and Their Applications, pp. 36-37, 2000.
[28] de Castro, L. N. - Timmis, J. I. Artificial Immune
Systems: A New Computational Intelligence Approach, Springer-Verlag, London, September, 357 p., 2002.
[29] Kim, J. - Bentley, P. “The Artificial Immune Model
for Network Intrusion Detection, 7th European Conference on Intelligent Techniques and Soft Computing EUFIT’99), Aachen, Germany, 1999. [30] Forrest, S., Hofmeyr, S. A. Immunology as
information processing. In Design Principles for Immune Systems and Other Distributed Autonomous Systems. Oxford University Press, New York, 2001. [31] Twycross, J. An Immune System Approach to
Document Classification. Master Thesis, University of Sussex, Hewlet-Pacard Research Labs, Bristol, 2002. [32] Carter, J. H., The Immune system as a model for
classification and pattern recognition. Journal of the American Informatics Association, vol. 7, 2000. [33] Brownlee, J. The misunderstood artificial immune
system. Technical report No. 3-01. January, 2005. [34] Janakova, H. Text categorization with feature
dictionary – problem of Czech language. WSEAS TRANSACTIONS on INFORMATION SCIENCE AND APPLICATIONS, Issue 1, Volume 1, July 2004, s. 368 - 372, ISSN 1790-0832
软件公司市场部的工作职责 进入IT行业，更准确的说是进入ERP行业，很真切的感受到行业的不同对市场部岗位设置要求的不同。 传统营销理论谈的是：4P，price价格、product产品、place渠道、promotion促销，市场部的主要工作职责可能也是围绕这些方面。但ERP软件公司很不同，比如：product：一套软件产品的生命周期通常在3-5年，软件的开发、需求确定等往往由技术部门主导，虽然有的大公司设有产品经理（理论上说也属于市场范畴），但大部分中小软件企业的市场部不包括产品开发该职能。Price：目前随着ERP市场竞争的激烈，随着客户成熟度的提高，目前一套ERP产品的售价往往取决与客户的议价能力、预算范围及竞争压力。在此方面，市场部起到的作用也不是非常大。Place：如果软件厂商没有设置代理商，市场部也没有用武之地了。Promotion：ERP产品面对的客户都是企业组织，属于组织购买行为，不是普通的广告、促销就能打动的了的。 那么软件公司是不是就没有必要设置市场部了？ 当然不是，而是在ERP行业，市场部的职责不同于其他行业。 对ERP等软件公司而言，最重要的是品牌建设及销售促进。软件公司的市场部，更应该以4C理论为指导：customer needs，cost，communication，convience。 细化一下，软件公司市场部的主要工作职责可以分为一下几类： 1、市场调研：关注行业动态，了解目标市场发展趋势及技术趋势等。 2、竞争者分析：对竞争对手历史、技术实力、成功失败案例、商务政策等要有一个基本的认识和了解。分为几个层次：知道竞争对手是谁；知道竞争对手做了什么；知道竞争对手未来会做什么；影响竞争对手策略。 3、品牌建设与推广。包括媒体宣传、网络营销、参加行业展会论坛等。 4、市场活动的策划与组织。 5、公关关系的维护，包括政府、协会、媒体等。 6、合作伙伴关系维护。 7、客户关系维护。软件公司更多的是关系影响，塑造标杆客户等非常重要。
1、人称代词顺口溜：人称代词有两类，一类主格一类宾；主格代词本领大，一切动作由它发；宾格代词不动脑，介动之后跟着跑。 2、物主代词顺口溜：物主代词不示弱，带着‘白勺’来捣乱；形容词性物主代，抓住名词不放松； 1、人称代词的主格在句子中作主语或主语补语。一般在句首，动词前。 例如：John waited a while but eventually he went home. 约翰等了一会儿，最后他回家了。 John hoped the passenger would be Mary and indeed it was she. 约翰希望那位乘客是玛丽，还真是她。 说明：在复合句中，如果主句和从句主语相同，代词主语要用在从句中，名词主语用在主句中。在电话用语中常用主格。 例如：When he arrived, John went straight to the bank. 约翰一到就直接去银行了。 I wish to speak to Mary. This is she. 我想和玛丽通话，我就是玛丽。 2、人称代词的宾格在句中作宾语或表语，在动词或介词后。 例如：Do you know him？(作宾语) 你认识他吗？ Who is knocking at the door？It’s me. (作表语) 是谁在敲门？是我。 说明：单独使用的人称代词通常用宾格，即使它代表主语时也是如此。 例如：I like English. Me too. 我喜欢英语。我也喜欢。 3、注意：在动词be 或to be 后的人称代词视其前面的名词或代词而定。 例如：I thought it was she.我以为是她。(主格----主格) I thought it to be her.(宾格----宾格) I was taken to be she.我被当成了她。(主格----主格) They took me to be her.他们把我当成了她。(宾格----宾格) 4、人称代词并列时的排列顺序 1）单数人称代词并列作主语时，其顺序为： 第二人称→第三人称→第一人称 即you and I he/she/it and I you, he/she/it and I 顺口溜：第一人称最谦虚，但若错误责任担，第一人称学当先。 例如：It was I and John that made her angry. 2）复数人称代词作主语时，其顺序为： 第一人称→第二人称→第三人称 即we and you you and they we, you and they
With的用法全解 with结构是许多英语复合结构中最常用的一种。学好它对学好复合宾语结构、不定式复合结构、动名词复合结构和独立主格结构均能起很重要的作用。本文就此的构成、特点及用法等作一较全面阐述，以帮助同学们掌握这一重要的语法知识。 一、 with结构的构成 它是由介词with或without+复合结构构成，复合结构作介词with或without的复合宾语，复合宾语中第一部分宾语由名词或代词充当，第二部分补足语由形容词、副词、介词短语、动词不定式或分词充当，分词可以是现在分词，也可以是过去分词。With结构构成方式如下： 1. with或without-名词/代词+形容词； 2. with或without-名词/代词+副词； 3. with或without-名词/代词+介词短语； 4. with或without-名词/代词 +动词不定式； 5. with或without-名词/代词 +分词。 下面分别举例： 1、 She came into the room，with her nose red because of cold.（with+名词+形容词，作伴随状语）
2、 With the meal over ， we all went home.（with+名词+副词，作时间状语） 3、The master was walking up and down with the ruler under his arm。（with+名词+介词短语，作伴随状语。） The teacher entered the classroom with a book in his hand. 4、He lay in the dark empty house，with not a man ，woman or child to say he was kind to me.（with+名词+不定式，作伴随状语）He could not finish it without me to help him.（without+代词 +不定式，作条件状语） 5、She fell asleep with the light burning.（with+名词+现在分词，作伴随状语） Without anything left in the with结构是许多英 语复合结构中最常用的一种。学好它对学好复合宾语结构、不定式复合结构、动名词复合结构和独立主格结构均能起很重要的作用。本文就此的构成、特点及用法等作一较全面阐述，以帮助同学们掌握这一重要的语法知识。 二、with结构的用法 with是介词，其意义颇多，一时难掌握。为帮助大家理清头绪，以教材中的句子为例，进行分类，并配以简单的解释。在句子中with结构多数充当状语，表示行为方式，伴随情况、时间、原因或条件（详见上述例句）。 1.带着，牵着…… (表动作特征)。如： Run with the kite like this.
岗位：项目经理 主要职责： 1、计划： a)项目范围、项目质量、项目时间、项目成本的确认。 b)项目过程/活动的标准化、规范化。 c)根据项目范围、质量、时间与成本的综合因素的考虑，进行项目的总体规划与阶段计划。 d)各项计划得到上级领导、客户方及项目组成员认可。 2、组织： a)组织项目所需的各项资源。 b)设置项目组中的各种角色，并分配好各角色的责任与权限。 c)定制项目组内外的沟通计划。（必要时可按配置管理要求写项目策划目录中的《项目沟通计划》） d)安排组内需求分析师、客户联系人等角色与客户的沟通与交流。 e)处理项目组与其它项目干系人之间的关系。 f)处理项目组内各角色之间的关系、处理项目组内各成员之间的关系。 g)安排客户培训工作。 3、领导： a)保证项目组目标明确且理解一致。 b)创建项目组的开发环境及氛围，在项目范围内保证项目组成员不受项目其它方面的影响。 c)提升项目组士气，加强项目组凝聚力。 d)合理安排项目组各成员的工作，使各成员工作都能达到一定的饱满度。 e)制定项目组需要的招聘或培训人员的计划。 f)定期组织项目组成员进行相关技术培训以及与项目相关的行业培训等。 g)及时发现项目组中出现的问题。 h)及时处理项目组中出现的问题。 4、控制 a)保证项目在预算成本范围内按规定的质量和进度达到项目目标。 b)在项目生命周期的各个阶段，跟踪、检查项目组成员的工作质量； c)定期向领导汇报项目工作进度以及项目开发过程中的难题。 d)对项目进行配置管理与规划。 e)控制项目组各成员的工作进度，即时了解项目组成员的工作情况，并能快速的解决项目组成员所碰到的难题。 f)不定期组织项目组成员进行项目以外的短期活动，以培养团队精神。 结语： 项目经理是在整个项目开发过程中项目组内对所有非技术性重要事情做出最终决定的人。 岗位：系统架构师（技术总监） 主要功能及职责：
with的用法大全----四级专项训练with结构是许多英语复合结构中最常用的一种。学好它对学好复合宾语结构、不定式复合结构、动名词复合结构和独立主格结构均能起很重要的作用。本文就此的构成、特点及用法等作一较全面阐述，以帮助同学们掌握这一重要的语法知识。 一、 with结构的构成 它是由介词with或without+复合结构构成，复合结构作介词with或without的复合宾语，复合宾语中第一部分宾语由名词或代词充当，第二部分补足语由形容词、副词、介词短语、动词不定式或分词充当，分词可以是现在分词，也可以是过去分词。With结构构成方式如下： 1. with或without-名词/代词+形容词; 2. with或without-名词/代词+副词; 3. with或without-名词/代词+介词短语; 4. with或without-名词/代词+动词不定式; 5. with或without-名词/代词+分词。 下面分别举例：
1、 She came into the room，with her nose red because of cold.(with+名词+形容词，作伴随状语) 2、 With the meal over ， we all went home.(with+名词+副词，作时间状语) 3、The master was walking up and down with the ruler under his arm。(with+名词+介词短语，作伴随状语。) The teacher entered the classroom with a book in his hand. 4、He lay in the dark empty house，with not a man ，woman or child to say he was kind to me.(with+名词+不定式，作伴随状语) He could not finish it without me to help him.(without+代词 +不定式，作条件状语) 5、She fell asleep with the light burning.(with+名词+现在分词，作伴随状语) 6、Without anything left in the cupboard， she went out to get something to eat.(without+代词+过去分词，作为原因状语) 二、with结构的用法 在句子中with结构多数充当状语，表示行为方式，伴随情况、时间、原因或条件(详见上述例句)。
with用法归纳 （1）“用……”表示使用工具，手段等。例如： ①We can walk with our legs and feet. 我们用腿脚行走。 ②He writes with a pencil. 他用铅笔写。 （2）“和……在一起”，表示伴随。例如： ①Can you go to a movie with me? 你能和我一起去看电影'>电影吗？ ②He often goes to the library with Jenny. 他常和詹妮一起去图书馆。 （3）“与……”。例如： I’d like to have a talk with you. 我很想和你说句话。 （4）“关于，对于”，表示一种关系或适应范围。例如： What’s wrong with your watch? 你的手表怎么了？ （5）“带有，具有”。例如： ①He’s a tall kid with short hair. 他是个长着一头短发的高个子小孩。 ②They have no money with them. 他们没带钱。 （6）“在……方面”。例如： Kate helps me with my English. 凯特帮我学英语。 （7）“随着，与……同时”。例如： With these words, he left the room. 说完这些话，他离开了房间。 [解题过程] with结构也称为with复合结构。是由with+复合宾语组成。常在句中做状语，表示谓语动作发生的伴随情况、时间、原因、方式等。其构成有下列几种情形： 1.with+名词(或代词)+现在分词 此时，现在分词和前面的名词或代词是逻辑上的主谓关系。 例如:1)With prices going up so fast, we can't afford luxuries. 由于物价上涨很快，我们买不起高档商品。（原因状语） 2)With the crowds cheering, they drove to the palace. 在人群的欢呼声中，他们驱车来到皇宫。（伴随情况） 2.with+名词(或代词)+过去分词 此时，过去分词和前面的名词或代词是逻辑上的动宾关系。
更多资料请访问.(.....) 软件部经理岗位职责 职位名称：软件部经理 所属部门：软件部 直属上级：技术总监 职位概要：负责软件工程项目的具体实施、自有产品及基础技术的开发。 工作内容：管理、组建公司开发团队，参与公司相关政策的制定；拟定和执行本部门年度、月度目标、工作计划及总结；设计、开发、维护、管理软件产品。 一、直接职责 1、拟定本部门年度、月度目标、工作计划及总结； 2、负责本部门的成本控制工作以及本部门员工的绩效考评及监督、管理工作； 3、参与技术业务制定流程及与其他部门的协调工作； 4、领导技术团队并组织实施年度工作计划，完成年度任务目标； 5、负责管理公司的整体核心技术，组织制定和实施重大技术决策和技术方案； 6、负责协调项目开发或实施的各个环节，把握项目的整体进度； 7、指导、审核项目总体技术方案，对各项目结果进行最终质量评估； 8、会同项目经理共同审核项目组内部测试计划，并组织项目组负责软件项目的后期维护工作； 9、针对部门的发展计划，向公司提供部门员工的培训要求，抓好部门员工的专业培训工作； 10、本部门的发展规划，组织审定部门各项技术标准，编制、完善软件开发流程； 11、负责与其他部门之间的沟通与协作，满足和协调公司各相关部门提出的系统更新、新产品等技术需求； 12、关注国内外软件市场的发展动向、最新技术及信息，组织内部技术交流。一三、配合市场部门开展工作，向市场部门提供必要的技术支持。
14、需求调研中，配合项目经理进行需求调研工作，并对生成的需求调研报告进行审核评定。 一五、明确文档编写种类及格式，对项目组需要生成的文档进行质量、数量和时间控制，并组织召开评审会； 16、制度本部门人员短期和长期需求计划，并配合行政部的人员招聘工作； 二、管理职责 1、抓好本部门项目组总结分析报告工作，定期进行项目分析、总结经验、找出存在的问题，提出改进工作的意见和建议，并组织本部门员工学习，为公司领导决策提供专题分析报告或综合分析资料； 2、开展公司的市场经营和客户服务工作，组织开展市场调查、经营分析，掌握竞争对手动态，及时组织竞争方案的制定和实施，确保公司在市场竞争中的主动； 3、组织实施公司机构和人员的调整设置、绩效考核及二级薪酬分配，提出员工的招聘和使用计划，保证公司内部考核、薪酬分配制度的合理完善及人力资源的有效配置，推进公司目标的实现。提供项目的设计方案，协助公司顺利接下项目； 4、参与工程项目的洽谈、制定和审核工作，对公司所签合同有关软件技术合同部分中工期、技术方案、软件合同额等方面提供技术支持； 5、推进公司企业文化建设，掌握员工主要思想动态，倡导队伍的创新和团队精神，提升公司核心竞争能力； 6、规范部门内部管理，提高员工整体技术水平，把握技术发展方向，使得技术发展方向与主流技术合拍； 7、定期组织部门人员培训，组建一个高效、有朝气、技术过硬的开发团队； 三、工作权限 1、对本部职责范围内的工作有指导、协调、监督管理的权力； 2、下属人员的工作态度，工作岗位等考核权、指导权、分配权； 3、所属人员的违纪、违规纠正权及事实处理权或处理申报权； 4、对本部门项目资金使用的额度内审核权； 5、对软件部人员及公司其他相关人员的技术培训提出指导建议权； 四、管辖范围 软件部所工作及总经理授权范畴。 五、工作标准（或要求） 1、严格遵守公司的各项管理制度，认真履行工作职责，行使公司给予的管理权力，软件部统一对外出口为软件部经理； 2、有效、合理的部署全部门的工作安排； 3、及时掌握客户的需求，针对项目方案做出分析； 4、对软件的整体设计以及调研进行审核及补救； 5、调动部门员工的工作热情，使部门形成良好风气； 6、处理部门突发事件，组织人员及时处置； 六、入职要求 1、计算机及其相关专业，大本以上学历。 2、4年以上软件开发经验及2年研发团队管理经验，有独立带领技术团队开发软件产品的成功案例； 3、精通各类型数据库，并能熟练编写数据库存储过程，触发器，熟悉、模式的项目开发； 4、有制造业项目经验，如仓库管理、车间管理、等；
独立主格篇 独立主格，首先它是一个“格”，而不是一个“句子”。在英语中任何一个句子都要有主谓结构，而在这个结构中，没有真正的主语和谓语动词，但又在逻辑上构成主谓或主表关系。独立主格结构主要用于描绘性文字中，其作用相当于一个状语从句，常用来表示时间、原因、条件、行为方式或伴随情况等。除名词/代词+名词、形容词、副词、非谓语动词及介词短语外，另有with或without短语可做独立主格，其中with可省略而without不可以。*注：独立主格结构一般放在句首，表示原因时还可放在句末；表伴随状况或补充说明时，相当于一个并列句，通常放于句末。 一、独立主格结构： 1. 名词/代词+形容词 He sat in the front row, his mouth half open. Close to the bank I saw deep pools, the water blue like the sky. 靠近岸时，我看见几汪深池塘，池水碧似蓝天。 2. 名词/代词+现在分词 Winter coming, it gets colder and colder. The rain having stopped, he went out for a walk.
The question having been settled, we wound up the meeting. 也可以The question settled, we wound up the meeting. 但含义稍有差异。前者强调了动作的先后。 We redoubled our efforts, each man working like two. 我们加倍努力，一个人干两个人的活。 3. 名词/代词+过去分词 The job finished, we went home. More time given, we should have done the job much better. *当表人体部位的词做逻辑主语时，不及物动词用现在分词，及物动词用过去分词。 He lay there, his teeth set, his hands clenched, his eyes looking straight up. 他躺在那儿，牙关紧闭，双拳紧握，两眼直视上方。 4. 名词/代词+不定式 We shall assemble at ten forty-five, the procession to start moving at precisely eleven. We divided the work, he to clean the windows and I to sweep the floor.
with用法小结 一、with表拥有某物 Mary married a man with a lot of money . 马莉嫁给了一个有着很多钱的男人。 I often dream of a big house with a nice garden . 我经常梦想有一个带花园的大房子。 The old man lived with a little dog on the lonely island . 这个老人和一条小狗住在荒岛上。 二、with表用某种工具或手段 I cut the apple with a sharp knife . 我用一把锋利的刀削平果。 Tom drew the picture with a pencil . 汤母用铅笔画画。 三、with表人与人之间的协同关系 make friends with sb talk with sb quarrel with sb struggle with sb fight with sb play with sb work with sb cooperate with sb I have been friends with Tom for ten years since we worked with each other, and I have never quarreled with him . 自从我们一起工作以来，我和汤姆已经是十年的朋友了，我们从没有吵过架。 四、with 表原因或理由 John was in bed with high fever . 约翰因发烧卧床。 He jumped up with joy . 他因高兴跳起来。 Father is often excited with wine . 父亲常因白酒变的兴奋。 五、with 表“带来”，或“带有,具有”，在…身上，在…身边之意
（岗位职责）软件公司各岗 位描述
软件公司岗位说明书 版本号V2010 岗位说明 岗位：项目经理 主要职责： 1、计划： a)项目范围、项目质量、项目时间、项目成本的确认。 b)项目过程/活动的标准化、规范化。 c)根据项目范围、质量、时间和成本的综合因素的考虑，进行项目的总体规划和阶段计划。 d)各项计划得到上级领导、客户方及项目组成员认可。 2、组织： a)组织项目所需的各项资源。 b)设置项目组中的各种角色，且分配好各角色的责任和权限。 c)定制项目组内外的沟通计划。（必要时可按配置管理要求写项目策划目录中的《项目沟通计划》） d)安排组内需求分析师、客户联系人等角色和客户的沟通和交流。 e)处理项目组和其它项目干系人之间的关系。 f)处理项目组内各角色之间的关系、处理项目组内各成员之间的关系。
g)安排客户培训工作。 3、领导： a)保证项目组目标明确且理解壹致。 b)创建项目组的开发环境及氛围，于项目范围内保证项目组成员不受项目其它方面的影响。 c)提升项目组士气，加强项目组凝聚力。 d)合理安排项目组各成员的工作，使各成员工作均能达到壹定的饱满度。 e)制定项目组需要的招聘或培训人员的计划。 f)定期组织项目组成员进行关联技术培训以及和项目关联的行业培训等。 g)及时发现项目组中出现的问题。 h)及时处理项目组中出现的问题。 4、控制 a)保证项目于预算成本范围内按规定的质量和进度达到项目目标。 b)于项目生命周期的各个阶段，跟踪、检查项目组成员的工作质量； c)定期向领导汇报项目工作进度以及项目开发过程中的难题。 d)对项目进行配置管理和规划。 e)控制项目组各成员的工作进度，即时了解项目组成员的工作情况，且能快速的解决项目组成员所碰到的难题。 f)不定期组织项目组成员进行项目以外的短期活动，以培养团队精神。 结语：项目经理是于整个项目开发过程中项目组内对所有非技术性重要事情做出最终决定的人。
comparison的用法解析大全 comparison的意思是比较，比喻，下面我把它的相关知识点整理给大家，希望你们会喜欢！ 释义 comparison n. 比较;对照;比喻;比较关系 [ 复数 comparisons ] 词组短语 comparison with 与…相比 in comparison adj. 相比之下;与……比较 in comparison with 与…比较，同…比较起来 by comparison 相比之下，比较起来 comparison method 比较法 make a comparison 进行比较 comparison test 比较检验 comparison theorem 比较定理 beyond comparison adv. 无以伦比 comparison table 对照表 comparison shopping 比较购物;采购条件的比较调查 paired comp arison 成对比较 同根词 词根： comparing adj. comparative 比较的;相当的 comparable 可比较的;比得上的 adv. comparatively 比较地;相当地 comparably 同等地;可比较地 n.
comparative 比较级;对手 comparing 比较 comparability 相似性;可比较性 v. comparing 比较;对照(compare的ing形式) 双语例句 He liked the comparison. 他喜欢这个比喻。 There is no comparison between the two. 二者不能相比。 Your conclusion is wrong in comparison with their conclusion. 你们的结论与他们的相比是错误的。 comparison的用法解析大全相关文章： 1.by的用法总结大全
介词With 复合结构讲解及练习 with复合结构的作用：with复合结构在句子中作状语，表示原因、时间、条件、伴随、方式等. 1）We sat on the dry grass with our backs to the wall.（作伴随状语） 2）She could not leave with her painful duty unfulfilled.（作原因状语） 3）He lay in bed with his head covered.（作方式状语） 4）Jack soon fell asleep with the light still burning.（作伴随状语） 5）I won't be able to go on holiday with my mother being ill.（作原因状语） 6）He sat with his arms clasped around his knees.（作方式状语） 注：with复合结构在句子中还可以作定语，阅读下面的句子。 1）There was a letter for Lanny with a Swiss stamp on it.（作定语修饰letter） 2）It was a vast stretch of country with cities in the distance.（作定语修饰a stretch of country）1) with +宾语+ 现在（短分词语） When mother went into the house, she found her baby was sleeping in bed, with his lips moving. 当妈妈走进房子的时候，她发现自己的孩子正睡在床上，嘴唇一直在动。 My aunt lives in the room with the windows facing south. 我姑妈住在那间窗户朝南开的房间。 With winter coming on，it's time to buy warm clothes 2)with +宾语+ 过去分词（短语） With more and more forests damaged ,some animals and plants are facing the danger of dying out. 由于越来越多的森林遭到破坏，一些动植物正面临着灭绝的危险。 With his legs broken, he had to lie in bed for a long time. 他双腿都断了，只得长时间躺在床上。 3) with +宾语+ 不定式（短语） * With so many children to look after, the nurse is busy all the time. 有这么多的孩子需要照顾，保育员一直都很忙。 *With a lot of papers to correct, M r. Li didn’t attend the party. 李老师有许多试卷需要批改，所以没有参加聚会。 4) with +宾语+ 副词 * You should read with the radio off. 在看书的时候应该把收音机关掉。 * With the temperature up, we had to open all the windows. 气温上升，我们不得不打开所有的窗户。 5) with +宾语+形容词 *With the window open, I felt a bit cold. 窗户开着，我感到有点冷。 * It was cold outside , the boy ran into the room with his nose red. 外面天气很冷，那个男孩跑进了屋子时，鼻子红红的。 6) with +宾语+ 介词短语 * The woman with a baby in her arms is getting on the bus. 怀里抱着婴儿的那位妇女正在上车。 * John starts to work very clearly in the morning and goes on working until late in the afternoon with a break at midday . 约翰早上开始工作，中午稍作休息后又接着工作到下午稍晚些时候。
with[wIT] prep.1.与…(在)一起，带着：Come with me. 跟我一起来吧。/ I went on holiday with my friend. 我跟我朋友一起去度假。/ Do you want to walk home with me? 你愿意和我一道走回家吗 2.(表带有或拥有)有…的，持有，随身带着：I have no money with me. 我没有带钱。/ He is a man with a hot temper. 他是一个脾气暴躁的人。/ We bought a house with a garden. 我们买了一座带花园的房子。/ China is a very large country with a long history. 中国是一个具有历史悠久的大国。3.(表方式、手段或工具)以，用：He caught the ball with his left hand. 他用左手接球。/ She wrote the letter with a pencil. 她用铅笔写那封信。4.(表材料或内容)以，用：Fill the glass with wine. 把杯子装满酒。/ The road is paved with stones. 这条路用石头铺砌。5.(表状态)在…的情况下，…地：He can read French with ease. 他能轻易地读法文。/ I finished my homework though with difficulty. 虽然有困难，我还是做完了功课。6.(表让步)尽管，虽然：With all his money, he is unhappy. 尽管他有钱，他并不快乐。/ With all his efforts, he lost the match. 虽然尽了全力，他还是输了那场比赛。7.(表条件)若是，如果：With your permission, I’ll go. 如蒙你同意我就去。8.(表原因或理由)因为，由于：He is tired with work. 他工作做累了。/ At the news we all jumped with joy. 听到这消息我们都高兴得跳了起来。9.(表时间)当…的时候，在…之后：With that remark, he left. 他说了那话就离开了。/ With daylight I hurried there to see what had happened. 天一亮我就去那儿看发生了什么事。10. (表同时或随同)与…一起，随着：The girl seemed to be growing prettier with each day. 那女孩好像长得一天比一天漂亮。11.(表伴随或附带情况)同时：I slept with the window open. 我开着窗户睡觉。/ Don’t speak with your mouth full. 不要满嘴巴食物说话。12.赞成，同意：I am with you there. 在那点上我同你意见一致。13.由…照看，交…管理，把…放在某处：I left a message for you with your secretary. 我给你留了个信儿交给你的秘书了。/ The keys are with reception. 钥匙放在接待处。14 (表连同或包含)连用，包含：The meal with wine came to ￡8 each. 那顿饭连酒每人8英镑。/ With preparation and marking a teacher works 12 hours a day. 一位老师连备课带批改作业每天工作12小时。15. (表对象或关系)对，关于，就…而言，对…来说：He is pleased with his new house. 他对他的新房子很满意。/ The teacher was very angry with him. 老师对他很生气。/ It’s the same with us students. 我们学生也是这样。16.(表对立或敌对)跟，以…为对手：The dog was fighting with the cat. 狗在同猫打架。/ He’s always arguing with his brother. 他老是跟他弟弟争论。17.(在祈使句中与副词连用)：Away with him! 带他走！/ Off with your clothes! 脱掉衣服！/ Down with your money! 交出钱来！ 【用法】1.表示方式、手段或工具等时(=以，用)，注意不要受汉语意思的影响而用错搭配，如“用英语”习惯上用in English，而不是with English。2.与某些抽象名词连用时，其作用相当于一个副词：with care=carefully 认真地/ with kindness=kindly 亲切地/ with joy=joyfully 高兴地/ with anger=angrily 生气地/ with sorrow=sorrowfully 悲伤地/ with ease=easily 容易地/ with delight=delightedly 高兴地/ with great fluency =very fluently 很流利地3.表示条件时，根据情况可与虚拟语气连用：With more money I would be able to buy it. 要是钱多一点，我就买得起了。/ With better equipment, we could have finished the job even sooner. 要是设备好些，我们完成这项工作还要快些。4.比较with 和as：两者均可表示“随着”，但前者是介词，后者是连词：He will improve as he grows older. 随着年龄的增长，他会进步的。/ People’s ideas change with the change of the times. 时代变了，人们的观念也会变化。5.介词with和to 均可表示“对”，但各自的搭配不同，注意不要受汉语意思的影响而用错，如在kind, polite, rude, good, married等形容词后通常不接介词with而接to。6.复合结构“with+宾语+宾语补足语”是一个很有用的结构，它在句中主要用作状语，表示伴随、原因、时间、条件、方式等；其中的宾语补足语可以是名词、形容词、副词、现在分词、过去分词、不定式、介词短语等：I went out with the windows open. 我外出时没有关窗户。/ He stood before his teacher with his head down. 他低着头站在老师面前。/ He was lying on the bed with all his clothes on. 他和衣躺在床上。/ He died with his daughter yet a schoolgirl. 他去世时，女儿还是个小学生。/ The old man sat there with a basket beside her. 老人坐在那儿，身边放着一个篮子。/ He fell asleep with the lamp burning. 他没熄灯就睡着了。/ He sat there with his eyes closed. 他闭目坐在那儿。/ I can’t go out with all these clothes to wash. 要洗这些衣服，我无法出去了。这类结构也常用于名词后作定语：The boy with nothing on is her son. 没穿衣服的这个男孩子是她儿子。 （摘自《英语常用词多用途词典》金盾出版社） - 1 -
软件公司岗位说明 书
软件公司岗位说明书 版本号 V
岗位说明 岗位:项目经理 主要职责: 1、计划: a)项目范围、项目质量、项目时间、项目成本的确认。 b)项目过程/活动的标准化、规范化。 c)根据项目范围、质量、时间与成本的综合因素的考虑,进行项目的总体规划与阶段计划。 d)各项计划得到上级领导、客户方及项目组成员认可。 2、组织: a)组织项目所需的各项资源。 b)设置项目组中的各种角色,并分配好各角色的责任与权限。 c)定制项目组内外的沟通计划。(必要时可按配置管理要求写项目策划目录中的<项目沟通计划>) d)安排组内需求分析师、客户联系人等角色与客户的沟通与交流。 e)处理项目组与其它项目干系人之间的关系。 f)处理项目组内各角色之间的关系、处理项目组内各成员之间的关系。 g)安排客户培训工作。
3、领导: a)保证项目组目标明确且理解一致。 b)创立项目组的开发环境及氛围,在项目范围内保证项目组成员不受项目其它方面的影响。 c)提升项目组士气,加强项目组凝聚力。 d)合理安排项目组各成员的工作,使各成员工作都能达到一定的饱满度。 e)制定项目组需要的招聘或培训人员的计划。 f)定期组织项目组成员进行相关技术培训以及与项目相关的行业培训等。 g)及时发现项目组中出现的问题。 h)及时处理项目组中出现的问题。 4、控制 a)保证项目在预算成本范围内按规定的质量和进度达到项目目标。 b)在项目生命周期的各个阶段,跟踪、检查项目组成员的工作质量; c)定期向领导汇报项目工作进度以及项目开发过程中的难题。 d)对项目进行配置管理与规划。 e)控制项目组各成员的工作进度,即时了解项目组成员的工作情况,并能快速的解决项目组成员所碰到的难题。
动名词是一种兼有动词和名词特征的非谓语动词。它可以支配宾语，也能被副词修饰。动名词有时态和语态的变化。 解释：动词的ing形式如果是名词，这个词称动名词。 特征：动词原形+ing构成，具有名词，动词一些特征 一、动名词的作用 动名词具有名词的性质，因此在句中可以作主语、表语、宾语、定语等。 1、作主语 Reading is an art. 读书是一种艺术。 Climbing mountains is really fun. 爬山是真有趣。 Working in these conditions is not a pleasure but a suffer. 在这种工作条件下工作不是愉快而是痛苦。 动名词作主语，有时先用it作形式主语，把动名词置于句末。这种用法在习惯句型中常用。如： It is no use/no good crying over spilt milk. 洒掉的牛奶哭也没用。 It is a waste of time persuading such a person to join us. 劝说这样的人加入真是浪费时间。 It was hard getting on the crowded street car. 上这种拥挤的车真难。 It is fun playing with children. 和孩子们一起玩真好。 There is no joking about such matters. 对这种事情不是开玩笑。 动名词作主语的几种类型 动名词可以在句子中充当名词所能充当的多种句子成分。在这里仅就动名词在句子中作主语的情况进行讨论。 动名词作主语有如下几种常见情况： 1. 直接位于句首做主语。例如： Swimming is a good sport in summer. 2. 用it 作形式主语，把动名词(真实主语)置于句尾作后置主语。 动名词做主语时，不太常用it 作先行主语，多见于某些形容词及名词之后。例如： It is no use telling him not to worry. 常见的能用于这种结构的形容词还有：better，wonderful，enjoyable，interesti ng，foolish，difficult，useless，senseless，worthwhile，等。 注意：important，essential，necessary 等形容词不能用于上述结构。 3. 用于“There be”结构中。例如： There is no saying when he'll come.很难说他何时回来。 4. 用于布告形式的省略结构中。例如： No smoking ( =No smoking is allowed (here) ). （禁止吸烟） No parking. （禁止停车） 5. 动名词的复合结构作主语