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2000 | Buch

Time Granularities in Databases, Data Mining, and Temporal Reasoning

verfasst von: Prof. Dr. Claudio Bettini, Prof. Dr. Sushil Jajodia, Prof. Dr. X. Sean Wang

Verlag: Springer Berlin Heidelberg

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Über dieses Buch

Calendar units, such as months and days, clock units, such as hours and seconds, and specialized units, such as business days and academic years, play a major role in a wide range of information system applications. System support for reasoning about these units, called granularities in this book, is important for the efficient design, use, and implementation of such applications. The book deals with several aspects of temporal information and provides a unifying model for granularities. It is intended for computer scientists and engineers who are interested in the formal models and technical development of specific issues. Practitioners can learn about critical aspects that must be taken into account when designing and implementing databases supporting temporal information. Lecturers may find this book useful for an advanced course on databases. Moreover, any graduate student working on time representation and reasoning, either in data or knowledge bases, should definitely read it.

Inhaltsverzeichnis

Frontmatter

Time Granularities

Frontmatter
1. Introduction
Abstract
The origin of the notion of time granularity probably goes back to the first efforts to measure time by human beings, and this really was a long time ago. From a carved eagle bone representing a lunar calendar found by archeologists, it seems that some 13,000 years ago Cro-Magnon men were the first to recognize the notions of time granularities and calendars.
Claudio Bettini, Sushil Jajodia, X. Sean Wang
2. Granularity Systems
Abstract
In this chapter we elaborate on the notion of time granularity, identifying structural properties, relationships, and symbolic representations. When time granularities are used within specific domains or applications, they are usually restricted to a subset of those satisfying the very general definition given in Chap. 1.
Claudio Bettini, Sushil Jajodia, X. Sean Wang

Applications to Databases

Frontmatter
3. Design of Temporal Databases with Multiple Granularities
Abstract
In the database area a large body of knowledge has been developed for addressing the problem of logical design:
Given a body of data and constraints on the data to be represented in a database, how do we decide on a suitable logical structure for these data?
Claudio Bettini, Sushil Jajodia, X. Sean Wang
4. Querying Temporal Databases with Multiple Views
Abstract
A prominent feature of temporal information is its richness in semantics associated with its temporal domain. When querying a temporal database, a user naturally assumes some “usual” semantics on stored temporal data. For instance, she expects that her bank account balance persists, i.e., the balance stays the same unless a transaction—deposit, withdrawal, or accrual of interest—is performed. Therefore, if she wishes to find the balance at a particular time and no balance amount is stored for that time, she looks for the balance of the last transaction that was performed before the time in question. Semantic assumptions may also involve different time granularities. For example, when the user asks for the account holder of a certain account in a particular month and account holders are stored in terms of days, she assumes that the answer is someone who is the account holder of that account on all the days within that month.
Claudio Bettini, Sushil Jajodia, X. Sean Wang

Reasoning with Time Granularities and Its Applications

Frontmatter
5. Constraint Reasoning
Abstract
We have seen in the previous chapters the impact that time granularities can have in the design and managing of temporal databases. In Chap. 4 time granularities are also used to perform a special kind of temporal reasoning, deriving implicit information from explicit temporal data. A different form of temporal reasoning involves deriving information on the temporal distance between two events given incomplete information on the distances among a larger set of events. This reasoning has been particularly investigated in artificial intelligence for planning and scheduling problems, but it can find interesting applications also in research areas closer to data management. In this chapter we investigate how the introduction of a rich model for time granularities can be integrated with the known techniques for this form of temporal reasoning.
Claudio Bettini, Sushil Jajodia, X. Sean Wang
6. An Application to Knowledge Discovery
Abstract
A huge amount of data is collected every day in the form of event time sequences. Common examples are recordings of different values of stock shares during a day, accesses to a computer via an external network, bank transactions, or events related to malfunctions in an industrial plant. These sequences register events with corresponding values of certain processes, and are valuable sources of information not only to search for a particular value or event at a specific time, but also to analyze the frequency of certain events, or sets of events related by particular temporal relationships. These types of analyses can be very useful for deriving implicit information from the raw data, and for predicting the future behavior of the monitored process.
Claudio Bettini, Sushil Jajodia, X. Sean Wang

Conclusion

Frontmatter
7. Open Issues and Research Directions
Abstract
In the previous chapters we have proposed a formal framework for the definition and representation of time granularities, we have investigated applications in temporal database design and in querying temporal databases under multiple views, and we have shown how reasoning with temporal constraints is affected when the constraints are given in terms of arbitrary granularities. The theoretical issues addressed in this book, as well as their applications, are not long established topics; they are still the subject of ongoing research. The purpose of this last chapter is to illustrate some of the problems we encountered while developing these theories and that we believe deserve further investigation. We also illustrate some of the applications that we could not investigate in detail in the book, but that we believe could greatly benefit from our results on granularity representation and reasoning.
Claudio Bettini, Sushil Jajodia, X. Sean Wang
8. Appendix: Proofs
Abstract
This appendix contains the proofs of the formal results presented in the book.
Claudio Bettini, Sushil Jajodia, X. Sean Wang
Backmatter
Metadaten
Titel
Time Granularities in Databases, Data Mining, and Temporal Reasoning
verfasst von
Prof. Dr. Claudio Bettini
Prof. Dr. Sushil Jajodia
Prof. Dr. X. Sean Wang
Copyright-Jahr
2000
Verlag
Springer Berlin Heidelberg
Electronic ISBN
978-3-662-04228-1
Print ISBN
978-3-642-08634-2
DOI
https://doi.org/10.1007/978-3-662-04228-1