A System Architecture for Temporally Oriented Data Management

Attention to the temporal aspects of data management has intensified in recent years,focusing on data models and related systems that are sensitive to the ubiquitous temporal aspects of data. Both the growing need for easier access to historical data, as well as the imminent availability of mass storage devices, are makingthis apromisingbranchof database research, both practically and theoretically. In this paper we summarize the main results of recent research on temporally sensitive data models, discuss the lessons learned in their development, and assess the prospects and dimculties involved in incorporating a temporal dimension into database management systems (TODBs). Inparticular, three system levels are identified: the external userview of the database; an intermediate view closer to the structure of an existing data model; and an internal or implementation view defined interms of low level data structures. This general architecture coherently incorporates a variety of related research results and development experiences, and serves as the framework for theoretical and implementation research into such system

Theoretical framework of temporal databases.

by Lam Wing Hee.Thesis (M.Phil.)--Chinese University of Hong Kong, 1991.Bibliography: leaves [56]-59.List of Figures --- p.vAcknowledgements --- p.viChapter 1. --- Introduction --- p.1Chapter 1.1 --- Historical Data and Temporal Databases --- p.1Chapter 1.2 --- Valid Time and Transaction Time --- p.3Chapter 1.2.1 --- Snapshot Databases --- p.3Chapter 1.2.2 --- Rollback Databases --- p.4Chapter 1.2.3 --- Historical Databases --- p.6Chapter 1.2.4 --- Temporal Databases --- p.7Chapter 1.3 --- Literature Review --- p.8Chapter 1.3.1 --- Data Models --- p.9Chapter 1.3.2 --- Query Languages --- p.11Chapter 1.3.3 --- Logical Design --- p.13Chapter 2. --- The Temporal Relational Data Model --- p.14Chapter 2.1 --- The Temporal Relational Data Model - Informal Description --- p.14Chapter 2.2 --- The Temporal Relational Data Model - Formal Description --- p.15Chapter 2.2.1 --- Valid and Transaction Time Intervals --- p.16Chapter 2.2.2 --- "Attributes, Tuples and Temporal Relations" --- p.16Chapter 2.3 --- What is a Key in Temporal Relations? --- p.17Chapter 3. --- The Temporal Relational Algebra --- p.20Chapter 3.1 --- Operations in the Temporal Relational Algebra --- p.20Chapter 3.1.1 --- Union and Set Difference --- p.21Chapter 3.1.2 --- Selection --- p.21Chapter 3.1.3 --- Projection --- p.23Chapter 3.1.4 --- Join --- p.24Chapter 3.1.4.1 --- Natural Join --- p.25Chapter 3.2 --- Temporal Relational Algebra and TempSQL --- p.30Chapter 4. --- Classical Data Dependencies in Temporal Relations --- p.32Chapter 4.1 --- Functional Dependency in the Temporal Relational Model --- p.32Chapter 4.2 --- Multivalued Dependency in the Temporal Relational Model --- p.33Chapter 4.3 --- Relationship with Snapshot Data Dependencies --- p.34Chapter 4.4 --- Lossless Decomposition --- p.35Chapter 5. --- Asynchronous Dependency --- p.39Chapter 5.1 --- Asynchronous Dependency --- p.40Chapter 5.2 --- Asynchronous Normal Form --- p.41Chapter 5.3 --- Generalized Form of Data Dependency --- p.42Chapter 5.3.1 --- Embedded Implicational Dependency --- p.43Chapter 5.3.2 --- Algebraic Dependency --- p.45Chapter 5.4 --- Asynchronous Dependency versus Synchronous Dependency --- p.46Chapter 6. --- Conclusions --- p.48Chapter 6.1 --- Summary of the Thesis --- p.48Chapter 6.2 --- Unsolved Problems and Research Directions --- p.49Chapter 6.2.1 --- Equivalent Representations in the Temporal Relational Model --- p.49Chapter 6.2.2 --- The Notion of 'Completeness' of Temporal Query Languages --- p.50Chapter 6.2.3 --- Logical Basis for Temporal Data Models and Languages --- p.51Chapter 6.2.4 --- Other Temporal Dependencies --- p.51Chapter 6.2.5 --- Research Directions in Topics other than Theory --- p.52Appendix Proofs of Theorems --- p.53Bibliography --- p.5

Tense, aspect and temporal reference

English exhibits a rich apparatus of tense, aspect, time adverbials and other expressions that can be used to order states of affairs with respect to each other, or to locate them at a point in time with respect to the moment of speech. Ideally one would want a semantics for these expressions to demonstrate that an orderly relationship exists between any one expression and the meanings it conveys. Yet most existing linguistic and formal semantic accounts leave something to be desired in this respect, describing natural language temporal categories as being full of ambiguities and indeterminacies, apparently escaping a uniform semantic description. It will be argued that this anomaly stems from the assumption that the semantics of these expressions is directly related to the linear conception of time familiar from temporal logic or physics - an assumption which can be seen to underly most of the current work on tense and aspect. According to these theories, the cognitive work involved in the processing of temporal discourse consists of the ordering of events as points or intervals on a time line or a set of time lines. There are, however, good reasons for wondering whether this time concept really is the one that our linguistic categories are most directly related to; it will be argued that a semantics of temporally referring expressions and a theory of their use in defining the temporal relations of events require a different and more complex structure underlying the meaning representations than is commonly assumed. A semantics will be developed, based on the assumption that categories like tense, aspect, aspectual adverbials and propositions refer to a mental representation of events that is structured on other than purely temporal principles, and to which the notion of a nucleus or consequentially related sequence of preparatory process, goal event and consequent state is central. It will be argued that the identification of the correct ontology is a logical preliminary to the choice of any particular formal representation scheme, as well as being essential in the design of natural language front-ends for temporal databases. It will be shown how the ontology developed here can be implemented in a database that contains time-related information about events and that is to be queried by means of natural language utterances

Partitioned storage for temporal databases

Efficiently maintaining history data on line together with current data is difficult. This paper discusses one promising approach, the temporally partitioned store. The current store contains current data and possibly some history data, while the history store holds the rest of the data. The two stores can utilize different storage formats, and even different storage media, depending on the individual data characteristics. We discuss various issues on the temporally partitioned store, investigate several formats for the history store, and evaluate their performance on a set of sample queries

ASSESSING THE TEMPORAL DIFFERENTIATION OF ATTRIBUTES AS AN IMPLEMENTATION STRATEGY FOR A TEMPORALLY ORIENTED RELATIONAL DBMS

Temporally Oriented Databases (TODBs) are database systems in which both historical and current data are accessed and treated with full symmetry. The growing interest in such systems is manifested recently in a number of research efforts focusing on a wide set of issues, ranging from the study of abstract conceptual models to the practical implementation of working systems. Attempts to implement TODBs have so far been at best preliminary, characterized by an ad hoc flavor, or have had a very limited scope. This dissertation research is an attempt to design a general purpose relational Temporally-Oriented Database Management System (TDMS), and examine the feasibility of its implementation along current theoretical concepts. The users view data in a TDMS as a temporally oriented, three dimensional cube; this is, in fact, implemented as a two layered data structure. The implementation model interrelates the external user view with an underlying functional view of the data , and specifies on the translation between these layers. The major principle in the implementation is the differentiation of attributes according to their temporal variation . This research uses this concept as an implementation strategy of TDMSs, and assesses this approach for dealing with the following primary questions: efficient ways to store and retrieve data, the integrity constraints needed to maintain the database consistency and the definitions and implementations of temporal operations in such systems. Further validation of the model was achieved through the development of a TDMS prototype. The prototype was developed using INGRES commands embedded in PASCAL programs on VAX/VMS, and provides a test bed for further studies of temporally oriented information systems.Information Systems Working Papers Serie

Exploring mobile trajectories: An investigation of individual spatial behaviour and geographic filters for information retrieval - Volume 1

This section presents both quantitative results, related to the performance of prediction surfaces according to the evaluation criteria described in section 3.5, and qualitative results, based upon testing geographic filters for information retrieval in an outdoor mobile computing environment as part of a major user evaluation study in the Swiss National Park, in the Summer of 2004. The chapter begins with quantitative evaluation, where three test scenarios are described (walking, driving and daily migration), followed by a description of the systematic variation in the temporal component of prediction, designed to compare short-term (10 minutes) and long-term (60 minutes) predictions into future. Next, three geographic filters described in the methodology are introduced as prediction surfaces. The three filters are spatial proximity, temporal proximity and speed-heading predictions. For each approach, the prediction input parameters were varied in a systematic way to uncover the impact of buffer size and distance decay functions (for spatial proximity prediction surfaces), the ‘recent behaviour period’, temporal weighting and decay function (for speed-heading prediction surfaces) and the time budget and enclosing function (for temporal proximity prediction surfaces). Next, the results are presented and analysed to describe, explain and contrast the characteristics of each prediction approach. This is followed by a brief assessment of the suitability of each approach to the scenarios in which it was tested. Overall, predictions based upon temporal proximity are found to be more effective than speed-heading predictions, which in turn out are more effective than predictions based upon spatial proximity. Finally, the chapter concludes with the results of the user evaluation study, which suggests that users of mobile information retrieval tools found the implemented “search ahead” filter useful, are receptive to the idea of other geographic filters, and benefit from the use of personalised geographic information with a spatial and temporal component