We present an efficient indexing method to locate 1-dimensional subsequences witbin a collection of sequences, such that the subsequences match a given (query) pattern within a specified tolerance. The idea is to map each data sequence into a small set of multidimensional rectangles in feature space. Then, these rectangles can be readily indexed using traditional spatial access methods, like the R*-tree [9]. In more deteil, we use a sliding window over the data sequence and extract its features; the result is a trail in feature space. We propose an efficient and effective algorithm to divide such trails into sub-trails, which are subsequently represented by their Minimum Bounding Rectangles (MBRs). We also examine queries of varying lengths, and we show how to handle each case efficiently. We implemented our method and carried out experiments on synthetic and real data (stock price movements). We compared the method to sequential scanning, which is the only obvious competitor. The results were excellent: our method accelerated the search time from 3 times up to 100 times.

Mining information and knowledge from large databases has been recognized by many researchers as a key research topic in database systems and machine learning, and by many industrial companies as an important area with an opportunity of major revenues. Researchers in many different fields have shown great interest in data mining. Several emerging applications in information providing services, such as data warehousing and on-line services over the Internet, also call for various data mining techniques to better understand user behavior, to improve the service provided, and to increase the business opportunities. In response to such a demand, this article is to provide a survey, from a database researcher's point of view, on the data mining techniques developed recently. A classification of the available data mining techniques is provided and a comparative study of such techniques is presented.

A temporal database contains time-varying data. In a real-time database transactions have deadlines or timing constraints. In this paper we review the substantial research in these two heretofore separate research areas. We first characterize the time domain, then investigate temporal and real-time data models. We evaluate temporal and real-time query languages along several dimensions. Temporal and real-time DBMS implementation is examined. We conclude with a summary of the major accomplishments of the research to date, and list several research questions that should be addressed next. Keywords: object-oriented database, relational databases, query language, temporal data model, time-constrained database, transaction time, user-defined time, valid time 1 Introduction Time is an important aspect of all real-world phenomena. Events occur at specific points in time; objects and the relationships among objects exist over time. The ability to model this temporal dimension of the real worl...

Spatio-temporal databases deal with geometries changing over time. In general, geometries cannot only change in discrete steps, but continuously, and we are talking about moving objects. If only the position in space of an object is relevant, then moving point is a basic abstraction; if also the extent is of interest, then the moving region abstraction captures moving as well as growing or shrinking regions. We propose a new line of research where moving points and moving regions are viewed as three-dimensional (2D space + time) or higher-dimensional entities whose structure and behavior is captured by modeling them as abstract data types. Such types can be integrated as base (attribute) data types into relational, object-oriented, or other DBMS data models; they can be implemented as data blades, cartridges, etc. for extensible DBMSs. We expect these spatio-temporal data types to play a similarly fundamental role for spatio-temporal databases as spatial data types have played for sp...

Although “now ” is expressed in SQL as CURRENT_TIMESTAMP within queries, this value cannot be stored in the database. However, this notion of an ever-increasing current-time value has been reflected in some temporal data models by inclusion of database-resident variables, such as “now”, “until-changed, ” “�, ” “@, ” and “–”. Time variables are very desirable, but their use also leads to a new type of database, consisting of tuples with variables, termed a variable database. This article proposes a framework for defining the semantics of the variable databases of the relational and temporal relational data models. A framework is presented because several reasonable meanings may be given to databases that use some of the specific temporal variables that have appeared in the literature. Using the framework, the article defines a useful semantics for such databases. Because situations occur where the existing time variables are inadequate, two new types of modeling entities that address these shortcomings, timestamps that we call now-relative and now-relative indeterminate, are introduced and defined within the framework. Moreover, the article provides a foundation, using algebraic

An overview is presented of the issues in building spatial databases. The focus is on data models and query processing. Query optimization in a spatial environment is also briefly discussed. Keywords and phrases: spatial databases, data models, spatial query processing, spatial query optimization, relational databases. This work was supported in part by the National Science Foundation under Grant IRI--9017393. To appear in Modern Database Systems: The Object Model, Interoperability, and Beyond, W. Kim, ed., Addison Wesley/ACM Press, Reading, MA, 1994. 1 Introduction Not so long ago the term database management system (DBMS) was a euphemism for distinguishing commercial applications (e.g., banking, insurance, etc.) from scientific applications (e.g., number crunching). Today the distinction is rapidly disappearing as users try to come to grips with an information explosion that increasingly involves the world around them. Some new application areas include geographic information sys...

. This paper presents a symbolic formalism for modeling and retrieving video data via the moving objects contained in the video images. The model integrates the representations of individual moving objects in a scene with the time-varying relationships between them by incorporating both the notions of object tracks and temporal sequences of PIRs (projection interval relationships). The model is supported by a set of operations which form the basis of a moving object algebra. This algebra allows one to retrieve scenes and information from scenes by specifying both spatial and temporal properties of the objects involved. It also provides operations to create new scenes from existing ones. A prototype implementation is described which allows queries to be specified either via an animation sketch or using the moving object algebra. Keywords: Multimedia Retrieval, Moving objects Model, Content-based image retrieval, Symbolic video model 1. Introduction In recent years, we have witnessed a...

Benchmarking of spario-temporal databases is an issue of growing importance. In case large real data sets are not available, benchmarking requires the generation of artificial data sets following the real-world behavior of spatial objects that change their locations, shapes and sizes over time. Only a few innovative papers have recently addressed the topic of spario-temporal data generators. However, all existing approaches do not consider several important aspects of continuously changing regional data. In this report, a new generator, called generator of time-evolving regional data (G-TERD), for this class of data is presented. The basic concepts that determine the function of G-TERD are the structure of complex 2-D regional objects, their color, maximum speed, zoom and rotation-angle per time slot, the influence of other moving or static objects on the speed and on the moving direction of an object, the position and movement of the scene-observer, the statistical distribution of each changing factor and finally, time. Apart from these concepts, the operation and basic algorithmic issues of GTERD are presented. In the framework developed, the user can control the generator response by setting several parameters values. To demonstrate the use of G-TERD, the generation of a number of sample data sets is presented and commented. The source code and a visualization tool for using and testing the new generator are available on the Web. Thus, it is easy for the user to manipulate the generator according to specific application requirements and at the same time to examine the reliability of the underlying generalized data model.

There is a wide range of scientific application domains requiring sophisticated management of spatio-temporal data. However, existing database management systems offer very limited (if any at all) support for managing such data. Thus, it is left to the researchers themselves to repeatedly code this management into each application. Besides being a time consuming task, this process is bound to introduce errors and increase the complexity of application management and data evolution. This paper addresses this very point. We present an extensible framework, based on extending an object-oriented database system, with kernel spatio-temporal classes, data structures and functions, to provide support for the development of spatio-temporal applications. Even though the paper's arguments are centered on geographic applications, the proposed framework can be used in other application domains where spatial and temporal data evolution must be considered (e.g., Biology). 1 Introduction Data used i...

Introduction The large majority of current systems for handling geospatial information are static, concentrating on a single temporal snapshot, usually the current state. Changes in the application domain are tracked in the system by performing updates and erasing information on the past. In recent years there has evolved a body of research, both in the general database community (Snodgrass 1992) and in the spatial database community (Al Taha et al 1993) for adding temporal dimensions. That research addresses the issue of `time in the system', where the challenge is to provide computational models that enable past, current and future states of the application domain (valid time) and the system (transaction time) to be handled in the temporal database. Work presented in this chapter, however, is concerned with a different aspect of temporal systems, referred to as `the system in time', where we are concerned to handle in a dynamic system a model of the r