Iqbal A. Goralwalla

Modeling video temporal relationships in an object database management system

One of the key aspects of videos is the temporal relationship between video frames. In this paper we propose a tree-based model for specifying the temporal semantics of video data. We present a unique way of integrating our video model into an object database management system which has rich multimedia temporal operations. We further show how temporal histories are used to model video data, explore the video object base using object-oriented techniques. Such a seamless integration gives a uniform interface to end users. The integrated video object base management systems supports a broad range of temporal queries.

An object-oriented framework for temporal data models

Most of the database research on modeling time has concentrated on the definition of a particular temporal model and its incorporation into a (relational or object) database management system. This has resulted in quite a large number of different temporal models, each providing a specific set of temporal features. Therefore, the first step of this work is a design space for temporal models which accommodates multiple notions of time, thereby classifying design alternatives for temporal models. The design space is then represented by exploiting object-oriented features to model the different aspects of time. An object-oriented approach allows us to capture the complex semantics of time by representing it as a basic entity. Furthermore, the typing and inheritance mechanisms of object-oriented systems allow the various notions of time to be reflected in a single framework. The framework can be used to accommodate the temporal needs of different applications, and derive existing temporal models by making a series of design decisions through subclass specialization. It can also be used to derive a series of new more general temporal models that meet the needs of a growing number of emerging applications. Furthermore, it can be used to compare and analyze different temporal object models with respect to the design dimensions.

Temporal Extensions to a Uniform Behavioral Object Model

We define temporal extensions to a uniform, behavioral and functional object model by providing an extensible set of structural and behavioral abstractions to model various notions of time for different applications. We discuss the temporal semantics of inheritance by defining a lifespan behavior on objects in a collection. Finally, we give an elaborative example and show that temporal objects can be queried without adding any extra construct to the underlying query language.

Temporal granularity for unanchored temporal data

Granularity is an integral feature of bothanchored(e.g., 25 October 1995 , July 1996) andunanchored(e.g.,3 minutes, 6 hours 20 minutes , 5 days, 1 week) temporal data. In supporting temporal data that is specified in different gran ularities, numerous approaches have been proposed to deal with the issues of converting temporal data from one granularity to another. The emphasis, however, has only been on granularity conversions with respect to anchored temporal data. This is because a granularity in these approaches i s modeled as an anchoredpartitioning of the time axis, thereby making it difficult to deal with granularity conversions in u anchored temporal data. In this paper we provide a novel approach to the treatment of granularity in temporal data. A granularity is modeled as a special kind of unanchored temporal primitive that can be used as a unit of time. That is, a granularity is modeled as a unit unanchored temporal primitive. Granularities are accommodated within the context of calendarsand granularity conversions are presented and discussed in terms of unanchored durations of time. This allows us to consistently model and operate on unanchored temporal data that is comprised of different and mixed granularities. Specifically, we show how unanchored temporal data is represented, give procedures for converting the data to a given granularity, provide canonical forms for the data, an d describe how operations between the data are performed.

Experimenting with temporal relational databases

In this paper we describe an implementation of a temporal relational database management system based on attribute timestamping. The algebraic language of the system includes relational algebra operators, restructuring operators and temporrd operators. We then use this system to carry out experiments on the performance of different types of temporal databases: databases using attribute timestamping, databases using tuple timestamping where relations are in temporal normal form and databases using tuple timestamping where a single relation is used. We run sample queries against these types of temporal databases and measure the processing time of these queries. This study verifies that the major performance trade off between different types of temporaf databases is between the restructuring (unpack) operation needed in temporal databases using attribute timestamping and the join operation needed in temporal databases using tuple timestamping. Furthermore, the experiments show that keeping all temporal tuples in one single relation does not prove to be an effective alternative for temporal databases which use tuple timestamping.

Modeling temporal primitives: back to basics

The fundamental question about a temporal model is “what is its underlying temporal structure?” More specifically, what are the temporal primitives supported in the model, what temporal domains are available over these primitives, and whether the primitives are determinate or indeterminate? In this paper a simple, general framework for supporting temporal primitives (instants, intervals, sets of intervals) is presented. The framework allows seamless integration of dense and discrete temporal domains of time over a linearly ordered, unbounded point structure. The framework also provides a set-theoretic basis that allows uniform treatment of determinate and indeterminate temporal primitives.

Managing Schema Evolution Using a Temporal Object Model

The issues of schema evolution and temporal object models are generally considered to be orthogonal and are handled independently. This is unrealistic because to properly model applications that need incremental design and experimentation (such as CAD, software design process), the evolutionary histories of the schema objects should be traceable. In this paper we propose a method for managing schema changes by exploiting the functionality of a temporal object model. The result is a uniform treatment of schema evolution and temporal support for many object database management systems applications that require both.

Modeling medical trials in pharmacoeconomics using a temporal object model

Time is an inherent feature of many medical applications. These applications can also benefit from the support of object database management systems which better capture the semantics of the complex objects that arise in the medical domain. In this paper, we present a uniform behavioral temporal object model which includes a rich and extensible set of types and behaviors to support the various features of a medical application. We concentrate here on the application of pharmacoeconomic medical trials. Pharmacoeconomics is a field of medical economics in which the costs and outcomes of alternative treatments are assessed and compared, in order to establish which is the most appropriate treatment for a particular illness in a particular setting. We describe in detail the histories and timelines features of our temporal model and show how they can effectively be used to model a pharmacoeconomic trial. We then give an instance of a pharmacoeconomic trial as it would appear in the temporal object model and show, using queries, how a series of different behaviors could be used to retrieve various components of the instance. These components could then be used to assess the alternative treatments involved in the trial and determine their cost-effectiveness.

A framework for temporal data models: exploiting object-oriented technology

Most database research on modeling time has concentrated on the definition of a particular temporal model and its incorporation into a (relational or object) database management system. This has resulted in quite a large number of different temporal models, each providing a specific set of temporal features. This paper presents an object-oriented framework for temporal models which supports multiple notions of time. The framework can be used to accommodate the temporal needs of different applications and to derive existing temporal models by making a series of design decisions through subclass specialization. It can also be used to derive a series of new, more general temporal models that meet the needs of a growing number of emerging applications.

Modeling Time: Back to Basics

Most of the work in modeling time in information systems has concentrated on issues such as support for historical information and providing query facilities to manipulate such information. In doing so, some simplistic view of the underling nature of time has been assumed. However, the domain of time is far from being simplistic. In this paper, we outline the various issues which arise in modeling basic temporal entities and propose solutions to these issues. More speci cally, we note that the nature of temporal information can either be anchored (e.g., October 25; 1995) or unanchored (e.g., 1 week), and is usually available in multiple granularities (e.g., the airline ight departure and arrival times are usually given in minutes, while the history of the salary of an employee is usually recorded in days). Physical temporal information also needs to be represented in a manner so as to be human readable. This is achieved using calendars. In this work, we show how both anchored and unanchored temporal entities are represented within the context of calendars. We discuss how calendars provide relationships between multiple granularities and facilitate the conversion of anchored and unanchored times from one granularity to another. We also give the semantics of various operations on anchored and unanchored times.