John F. Roddick

A survey of temporal knowledge discovery paradigms and methods

With the increase in the size of data sets, data mining has recently become an important research topic and is receiving substantial interest from both academia and industry. At the same time, interest in temporal databases has been increasing and a growing number of both prototype and implemented systems are using an enhanced temporal understanding to explain aspects of behavior associated with the implicit time-varying nature of the universe. This paper investigates the confluence of these two areas, surveys the work to date, and explores the issues involved and the outstanding problems in temporal data mining.

A survey of schema versioning issues for database systems

Abstract Schema versioning is one of a number of related areas dealing with the same general problem—that of using multiple heterogeneous schemata for various database related tasks. In particular, schema versioning, and its weaker companion, schema evolution, deal with the need to retain current data and software system functionality in the face of changing database structure. Schema versioning and schema evolution offer a solution to the problem by enabling intelligent handling of any temporal mismatch between data and data structure. This survey discusses the modelling, architectural and query language issues relating to the support of evolving schemata in database systems. An indication of the future directions of schema versioning research is also given.

Association mining

The task of finding correlations between items in a dataset, association mining, has received considerable attention over the last decade. This article presents a survey of association mining fundamentals, detailing the evolution of association mining algorithms from the seminal to the state-of-the-art. This survey focuses on the fundamental principles of association mining, that is, itemset identification, rule generation, and their generic optimizations.

A bibliography of temporal, spatial and spatio-temporal data mining research

With the growth in the size of datasets, data mining has recently become an important research topic and is receiving substantial interest from both academia and industry. At the same time, a greater recognition of the value of temporal and spatial data has been evident and the first papers looking at the confluence of these two areas are starting to emerge. This short paper provides a few comments on this research and provides a bibliography of relevant research papers investigating temporal, spatial and spatio-temporal data mining.

Ant colony system with communication strategies

In this paper an ant colony system (ACS) with communication strategies is developed. The artificial ants are partitioned into several groups. Seven communication methods for updating the pheromone level between groups in ACS are proposed and work on the traveling salesman problem using our system is presented. Experimental results based on three well-known traveling salesman data sets demonstrate the proposed ACS with communication strategies are superior to the existing ant colony system (ACS) and ant system (AS) with similar or better running times.

TSQL2 language specification

This docuinent specifies a temporal extension to the SQL-92 language standard. The language is designated TSQLZ. The document is organized as follows. The next section indicates the starting point of the design, the SQL92 language. Section 4 lists the desired features on which the TSQL2 Language Design Committee reached consensus. Section 5 presents the major concepts underlying TSQL2. Compatibility with SQL-92 is the topic of Section 6. Section 7 briefly discusses how the language can be implemented. Subsequent sections specify the syntax of the language extensions.

Sequential pattern mining -- approaches and algorithms

Sequences of events, items, or tokens occurring in an ordered metric space appear often in data and the requirement to detect and analyze frequent subsequences is a common problem. Sequential Pattern Mining arose as a subfield of data mining to focus on this field. This article surveys the approaches and algorithms proposed to date.

ARMADA - An algorithm for discovering richer relative temporal association rules from interval-based data

Temporal association rule mining promises the ability to discover time-dependent correlations or patterns between events in large volumes of data. To date, most temporal data mining research has focused on events existing at a point in time rather than over a temporal interval. In comparison to static rules, mining with respect to time points provides semantically richer rules. However, accommodating temporal intervals offers rules that are richer still. In this paper we outline a new algorithm, ARMADA, to discover frequent temporal patterns and to generate richer interval-based temporal association rules. In addition, we introduce a maximum gap time constraint that can be used to get rid of insignificant patterns and rules so that the number of generated patterns and rules can be reduced. Synthetic datasets are utilized to assess the performance of the algorithm.

Advances in Conceptual Modeling

Traditionally product data and their evolving definitions, have been handled separately from process data and their evolving definitions. There is little or no overlap between these two views of systems even though product and process data arc inextricably linked over the complete software lifecycle from design to production. The integration of product and process models in an unified data model provides the means by which data could be shared across an enterprise throughout the lifecycle, even while that data continues to evolve. In integrating these domains, an object oriented approach to data modelling has been adopted by the CRISTAL (Cooperating Repositories and an Information System for Tracking Assembly Lifecycles) project. The model that has been developed is description-driven in nature in that it captures multiple layers of product and process definitions and it provides object persistence, flexibility, reusability, schema evolution and versioning of data elements. This paper describes the model that has been developed in CRISTAL and how descriptive meta-objects in that model have their persistence handled. It concludes that adopting a description-driven approach to modelling, aligned with a use of suitable object persistence, can lead to an integration of product and process models which is sufficiently flexible to cope with evolving data definitions. Ke)fwords: Description-Driven systems. Modelling change, schema evolution, versioning

An Updated Bibliography of Temporal, Spatial, and Spatio-temporal Data Mining Research

Data mining and knowledge discovery have become important issues for research over the past decade. This has been caused not only by the growth in the size of datasets but also in the availability of otherwise unavailable datasets over the Internet and the increased value that organisations now place on the knowledge that can be gained from data analysis. It is therefore not surprising that the increased interest in temporal and spatial data has led also to an increased interest in mining such data. This bibliography subsumes an earlier bibliography and shows that the value of investigating temporal, spatial and spatio-temporal data has been growing in both interest and applicability.