Abdullah Uz Tansel

A consensus glossary of temporal database concepts

This document contains definitions of a wide range of concepts specific to and widely used within temporal databases. In addition to providing definitions, the document also includes separate explanations of many of the defined concepts. Two sets of criteria are included. First, all included concepts were required to satisfy four relevance criteria, and, second, the naming of the concepts was resolved using a set of evaluation criteria. The concepts are grouped into three categories: concepts of general database interest, of temporal database interest, and of specialized interest. This document is a digest of a full version of the glossary1. In addition to the material included here, the full version includes substantial discussions of the naming of the concepts.The consensus effort that lead to this glossary was initiated in Early 1992. Earlier status documents appeared in March 1993 and December 1992 and included terms proposed after an initial glossary appeared in SIGMOD Record in September 1992. The present glossary subsumes all the previous documents. It was most recently discussed at the ARPA/NSF International Workshop on an Infrastructure for Temporal Databases, in Arlington, TX, June 1993, and is recommended by a significant part of the temporal database community. The glossary meets a need for creating a higher degree of consensus on the definition and naming of temporal database concepts.This document contains definitions of a wide range of concepts specific to and widely used within temporal databases. In addition to providing definitions, the document also includes separate explanations of many of the defined concepts. Two sets of criteria are included. First, all included concepts were required to satisfy four relevance criteria, and, second, the naming of the concepts was resolved using a set of evaluation criteria. The concepts are grouped into three categories: concepts of general database interest, of temporal database interest, and of specialized interest. This document is a digest of a full version of the glossary1. In addition to the material included here, the full version includes substantial discussions of the naming of the concepts.The consensus effort that lead to this glossary was initiated in Early 1992. Earlier status documents appeared in March 1993 and December 1992 and included terms proposed after an initial glossary appeared in SIGMOD Record in September 1992. The present glossary subsumes all the previous documents. It was most recently discussed at the ARPA/NSF International Workshop on an Infrastructure for Temporal Databases, in Arlington, TX, June 1993, and is recommended by a significant part of the temporal database community. The glossary meets a need for creating a higher degree of consensus on the definition and naming of temporal database concepts.

On an algebra for historical relational databases: two views

In search of the appropriate semantics -for the inclusion of structures and operations that ~111 meet the needs of a wide class of users interested in a database system supporting temporal views of their data, the paper includes both a discussion of many problems that must be addressed, and a proposal for an extended relational algebra The informal dlscusslon of Issues motivates the proposal to time-stamp the attributes of relations, rather than the tuples The proposed algebra incorporates this view with a general treatment of non-first-normal-form relations Note Parts I and II were written and submitted independently by their respective authors to the 1985 SIGMOD Conference, they have been combined at the invitation of the 1985 SIGMOD Program Committee The names of the authors are llsted In alphabetlcal order

Adding time dimension to relational model and extending relational algebra

Abstract A methodology for adding the time dimension to the relational model is proposed and relational algebra is extended for this purpose. We propose time-stamping attributes instead of adding time to tuples. Each attribute value is stored along with a time interval over which it is valid. Non-first normal form realations are used. A relation can have atomic, set-valued, triplet-valued, or set triplet-valued attributes. The last two types of attributes preserve the time (history). Furthermore, new algebraic operations are defined to extract information from historical relations. These operations convert one attribute type to another and do selection over the time dimension. Algebraic rules and identities for the new operations are also included.

An efficient algorithm to update large itemsets with early pruning

We present an efficient algorithm (UWEP) for updating large itemsets when new transactions are added to the set of old transactions. UWEP employs a dynamic lookahead strategy in updating the existing large itemsets by detecting and removing those that will no longer remain large after the contribution of the new set of transactions. It differs from the other update algorithms by scanning the existing database at most once and the new database exactly once. Moreover, it generates and counts the minimum number of candidates in the new database. The experiments on synthetic data show that UWEP outperforms the existing algorithms in terms of the candidates generated and counted.

Temporal relational data model

This paper incorporates a temporal dimension to nested relations. It combines research in temporal databases and nested relations for managing the temporal data in nontraditional database applications. A temporal data value is represented as a temporal atom; a temporal atom consists of two parts: a temporal set and a value. The temporal atom asserts that the value is valid over the time duration represented by its temporal set. The data model allows relations with arbitrary levels of nesting and can represent the histories of objects and their relationships. Temporal relational algebra and calculus languages are formulated and their equivalence is proved. Temporal relational algebra includes operations to manipulate temporal data and to restructure nested temporal relations. Additionally, we define operations to generate a power set of a relation, a set membership test, and a set inclusion test, which are all derived from the other operations of temporal relational algebra. To obtain a concise representation of temporal data (temporal reduction), collapsed versions of the set-theoretic operations are defined. Procedures to express collapsed operations by the regular operations of temporal relational algebra are included. The paper also develops procedures to completely flatten a nested temporal relation into an equivalent 1 NF relation and back to its original form, thus providing a basis for the semantics of the collapsed operations by the traditional operations on 1 NF relations.

Nested historical relations

The paper extends nested relations for managing temporal variation of complex objects. It combines the research in temporal databases and nested relations for nontraditional database applications. The basic modelling construct is a temporal atom as an attribute value. A temporal atom consists of two components, a value and temporal set which is a set of times denoting the validity period of the value. We define algebra operations for nested historical relations. Data redundancy in nested historical relations is also discussed and criteria for well-structured nested relations are established.

The expressive power of temporal relational query languages

The authors consider the representation of temporal data based on tuple and attribute timestamping. They identify the requirements in modeling temporal data and elaborate on their implications in the expressive power of temporal query languages. They introduce a temporal relational data model where N1NF relations and attribute timestamping are used and one level of nesting is allowed. For this model, a nested relational tuple calculus (NTC) is defined. They follow a comparative approach in evaluating the expressive power of temporal query languages, using NTC as a metric and comparing it with the existing temporal query languages. They prove that NTC subsumes the expressive power of these query languages. They also demonstrate how various temporal relational models can be obtained from the temporal relations by NTC and give equivalent NTC expressions for their languages. Furthermore, they show the equivalence of intervals and temporal elements (sets) as timestamps in their model.

A historical query language

Abstract A historical query language, hquel , is designed as a minimal extension to quel , which is the query language of the database management system ingres . In the underlying data model, each attribute value is stored along with the time interval over which the value is valid. hquel includes new range declarations, set theoretic expressions, and conditions. Aggregate functions of quel are also extended for historical data. A new clause, “when”, is added to aggregate function definition to make aggregation over the time dimension easier. Semantics of time is embedded within the quel semantics without any major modifications. hquel also manipulates set-valued attributes in N1NF relations.

A statistical interface for historical relational databases

A historical database is a vast source of data for statistical analysis. The paper presents a statistical interface for historical relational databases with an underlying data model which uses attribute time-stamping. The statistical interface we propose includes aggregate functions and transformations of data into tabular forms suitable for advanced statistical analysis and is capable of dealing with the subtle semantics of time and various issues associated with it. A new operation, called enumeration, is introduced into historical relational algebra. The enumeration operation derives a table of uniform data, for a set of specified time points or intervals, from a three dimensional historical relation. We also give a method for normalizing historical relations and aligning the time of their attributes.

Discovery of Association Rules in Temporal Databases

Temporal databases naturally contain a wealth of information that can be unearthed by knowledge discovery and data mining techniques. Discovering association rules in market basket data have been widely studied and many algorithms have been developed. In this study, we examine discovery of association rules in temporal databases. We use the enumeration operation of the temporal relational algebra to prepare the data for discovery of association rules. To observe the changes in association rules and their statistics over the time, we can apply an incremental association rule mining technique to a series of datasets obtained over consecutive time intervals