Information Retrieval. Course Material of the Course held
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Basic DBMS features
In this section, the basic concepts of database management systems (DBMS) (see e.g. Date 90], Ullman 88]) are described, and it is shown how these concepts apply to IRS. Besides the data model, the di erent types of DB consistency are described: Integrity means that the static relationships between di erent items stored in a database are maintained (e.g. that for each account in a bank database, there must be an address record for the person owning the account). Recovery is the ability of the DBMS to recover after a system failure and to return to a consistent database state. Concurrency control guarantees DB consistency when multiple users access the database, even when they perform concurrent updates. Security is a feature to restrict the rights of certain users to perform certain operations on speci c parts of the database, e.g. in order to make portions of the database inaccessible for some users.
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8 IR and Databases
8.1 Introduction : : : : : : : : : 8.2 Basic DBMS features : : : : 8.2.1 Data model : : : : : 8.2.2 Integrity : : : : : : : 8.2.3 Recovery : : : : : : : 8.2.4 Concurrency : : : : : 8.2.5 Security : : : : : : : 8.3 Modeling structure : : : : : 8.3.1 Relational model : : 8.3.2 NF2 model : : : : : : 8.4 Object-oriented databases : 8.4.1 Modeling structure : 8.4.2 Modeling behaviour :
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Chapter 8 IR and Databases
8.1 Introduction
The elds of databases (DB) and IR have evolved separately over a long period of time. This is mainly due to the di erence in the major topics that have been studied: whereas IR has concentrated on dealing with natural language text (i.e. \unformatted data") and retrieval under uncertainty, the focus in databases is on data models, query languages, database consistency and e ciency. Uncertainty in database systems has been only a minor research topic (see e.g. Lee 92], Barbara et al. 90], Prade & Testemale 84]). In the following, we will show how the aspects of data structure and consistency can be applied to IR systems. It will become clear that future IR systems will have to integrate these concepts. Figure 8.1 shows an exmaple document from a typical IR database. It is obvious that besides (natural language) text, this document also contains fact data of di erent sorts (e.g. dates, names of persons and institutions). So users may want to ask for this information, too. That is, besides documents, they want to search for other types of objects, too. There may be two di erent goals behind such a need: In order to enhance document retrieval, users may want to extract information baout di erent kinds of objects that occur in the current answer set, e.g. Which index terms occur most frequently in the answer set? or Which authors occur in the answer set?. Some current (commercial) IRS provide special commands for this type of queries. For some applications, there is a need to extract other information from the database, that is, the nal answer consists of objects which are not documents. For example, a researcher may search for colleagues working in a certain eld by asking Which institutions / authors work in this eld?, or he might be interested in historical aspects by posing the query When was most of the research in this area done? Current IRS do not support this type of queries. So we see that there seems to be a need for providing querying facilities that enable an user of an IRS to ask not only for documents, but for all types of objects occurring in an IR database. This goal can be accomplished only by introducing a powerful data model and query language which supports this type of queries. 2
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In this section, the basic concepts of database management systems (DBMS) (see e.g. Date 90], Ullman 88]) are described, and it is shown how these concepts apply to IRS. Besides the data model, the di erent types of DB consistency are described: Integrity means that the static relationships between di erent items stored in a database are maintained (e.g. that for each account in a bank database, there must be an address record for the person owning the account). Recovery is the ability of the DBMS to recover after a system failure and to return to a consistent database state. Concurrency control guarantees DB consistency when multiple users access the database, even when they perform concurrent updates. Security is a feature to restrict the rights of certain users to perform certain operations on speci c parts of the database, e.g. in order to make portions of the database inaccessible for some users.
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: : : : : : : : : : : : :
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: : : : : : : : : : : : :
: : : : : : : : : : : : :
: : : : : : : : : : : : :
8 IR and Databases
8.1 Introduction : : : : : : : : : 8.2 Basic DBMS features : : : : 8.2.1 Data model : : : : : 8.2.2 Integrity : : : : : : : 8.2.3 Recovery : : : : : : : 8.2.4 Concurrency : : : : : 8.2.5 Security : : : : : : : 8.3 Modeling structure : : : : : 8.3.1 Relational model : : 8.3.2 NF2 model : : : : : : 8.4 Object-oriented databases : 8.4.1 Modeling structure : 8.4.2 Modeling behaviour :
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2 3 3 4 5 5 6 7 7 10 12 13 16
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1
Chapter 8 IR and Databases
8.1 Introduction
The elds of databases (DB) and IR have evolved separately over a long period of time. This is mainly due to the di erence in the major topics that have been studied: whereas IR has concentrated on dealing with natural language text (i.e. \unformatted data") and retrieval under uncertainty, the focus in databases is on data models, query languages, database consistency and e ciency. Uncertainty in database systems has been only a minor research topic (see e.g. Lee 92], Barbara et al. 90], Prade & Testemale 84]). In the following, we will show how the aspects of data structure and consistency can be applied to IR systems. It will become clear that future IR systems will have to integrate these concepts. Figure 8.1 shows an exmaple document from a typical IR database. It is obvious that besides (natural language) text, this document also contains fact data of di erent sorts (e.g. dates, names of persons and institutions). So users may want to ask for this information, too. That is, besides documents, they want to search for other types of objects, too. There may be two di erent goals behind such a need: In order to enhance document retrieval, users may want to extract information baout di erent kinds of objects that occur in the current answer set, e.g. Which index terms occur most frequently in the answer set? or Which authors occur in the answer set?. Some current (commercial) IRS provide special commands for this type of queries. For some applications, there is a need to extract other information from the database, that is, the nal answer consists of objects which are not documents. For example, a researcher may search for colleagues working in a certain eld by asking Which institutions / authors work in this eld?, or he might be interested in historical aspects by posing the query When was most of the research in this area done? Current IRS do not support this type of queries. So we see that there seems to be a need for providing querying facilities that enable an user of an IRS to ask not only for documents, but for all types of objects occurring in an IR database. This goal can be accomplished only by introducing a powerful data model and query language which supports this type of queries. 2
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