TEMPORAL GENERALIZATION WITH DOMAIN GENERALIZATION GRAPHS

1999 ◽  
Vol 13 (02) ◽  
pp. 195-217 ◽  
Author(s):  
D. J. RANDALL ◽  
H. J. HAMILTON ◽  
R. J. HILDERMAN
Keyword(s):  
Partial Order ◽  
Relational Databases ◽  
Formal Specifications ◽  
Temporal Data ◽  
Calendar Date ◽  
Temporal Generalization ◽  
Domain Generalization Graphs

This paper addresses the problem of using domain generalization graphs to generalize temporal data extracted from relational databases. A domain generalization graph associated with an attribute defines a partial order which represents a set of generalization relations for the attribute. We propose formal specifications for domain generalization graphs associated with calendar (date and time) attributes. These graphs are reusable (i.e. can be used to generalize any calendar attributes), adaptable (i.e. can be extended or restricted as appropriate for particular applications), and transportable (i.e. can be used with any database containing a calendar attribute).

Managing Temporal Data

2009 ◽  
pp. 28-36 ◽  
Author(s):  
Abdullah Uz Tansel
Keyword(s):  
Relational Databases ◽  
Handling Time ◽  
Current Data ◽  
Temporal Databases ◽  
Time Varying ◽  
Temporal Data ◽  
Temporal Database ◽  
Organizational Intelligence ◽  
Relational Data Model ◽  
Future Data

In general, databases store current data. However,the capability to maintain temporal data is a crucial requirement for many organizations and provides the base for organizational intelligence. A temporal database maintains time-varying data, that is, past, present, and future data. In this chapter, we focus on the relational data model and address the subtle issues in modeling and designing temporal databases. A common approach to handle temporal data within the traditional relational databases is the addition of time columns to a relation. Though this appears to be a simple and intuitive solution, it does not address many subtle issues peculiar to temporal data, that is, comparing database states at two different time points, capturing the periods for concurrent events and accessing times beyond these periods, handling multi-valued attributes, coalescing and restructuring temporal data, and so forth, [Gadia 1988, Tansel and Tin 1997]. There is a growing interest in temporal databases. A first book dedicated to temporal databases [Tansel at al 1993] followed by others addressing issues in handling time-varying data [Betini, Jajodia and Wang 1988, Date, Darwen and Lorentzos 2002, Snodgrass 1999].

A Temporal Query System for Protocol-Directed Decision Support

1994 ◽  
Vol 33 (04) ◽  
pp. 358-370 ◽  
Author(s):  
A. K. Das ◽  
M. A. Musen
Keyword(s):  
Decision Support ◽  
Relational Databases ◽  
Query Language ◽  
Search Time ◽  
Relational Algebra ◽  
Temporal Data ◽  
Relational Data Model ◽  
Temporal Query ◽  
Query System ◽  
Temporal Algebra

Abstract:Chronus is a query system that supports temporal extensions to the Structured Query Language (SQL) for relational databases. Although the relational data model can store time-stamped data and can permit simple temporal-comparison operations, it does not provide either a closed or a sufficient algebra for manipulating temporal data. In this paper, we outline an algebra that maintains a consistent relational representation of temporal data and that allows the type of temporal queries needed for protocol-directed decision support. We also discuss how Chronus can translate between our temporal algebra and the relational algebra used for SQL queries. We have applied our system to the task of screening patients for clinical trials. Our results demonstrate that Chronus can express sufficiently all required temporal queries, and that the search time of such queries is similar to that of standard SQL.

scholarly journals Processing Archive Information in Digital University

2020 ◽  
Vol 35 ◽  
pp. 02003
Author(s):  
Alexander V. Baldin ◽  
Dmitriy V. Eliseev
Keyword(s):  
Human Resource ◽  
Relational Databases ◽  
Space Structure ◽  
Information Storage ◽  
Relational Model ◽  
Multidimensional Space ◽  
Data Archive ◽  
Temporal Data ◽  
Processing Data ◽  
Time Required

The article discusses methods of processing and storing data archive used in the digital university. Disadvantages of these methods are found. As a result, a fundamentally new method of processing and storing information archive in a constantly changing scheme database is proposed. This method uses mivar technologies. The multidimensional space structure has been developed to store the data archive. This multidimensional space describes the temporal relational model. For processing data, archive is proposed scheme for selecting the subspace and converting it into relations. A method of transformation of relational databases into multidimensional mivar space for efficient execution of operations on temporal data with changing structure is proposed. The transition to a multidimensional space allows us to describe the process of changing temporal data and their structure in a unified way. As a result, the time required to adapt the database schema and the redundancy of information storage are reduced. The results of this work are used in the human resource management database of BMSTU.

Managing Temporal Data

2011 ◽  
pp. 1461-1469
Author(s):  
Abdullah Uz Tansel
Keyword(s):  
Relational Databases ◽  
Handling Time ◽  
Current Data ◽  
Temporal Databases ◽  
Time Varying ◽  
Temporal Data ◽  
Temporal Database ◽  
Organizational Intelligence ◽  
Relational Data Model ◽  
Future Data

In general, databases store current data. However,the capability to maintain temporal data is a crucial requirement for many organizations and provides the base for organizational intelligence. A temporal database maintains time-varying data, that is, past, present, and future data. In this chapter, we focus on the relational data model and address the subtle issues in modeling and designing temporal databases. A common approach to handle temporal data within the traditional relational databases is the addition of time columns to a relation. Though this appears to be a simple and intuitive solution, it does not address many subtle issues peculiar to temporal data, that is, comparing database states at two different time points, capturing the periods for concurrent events and accessing times beyond these periods, handling multi-valued attributes, coalescing and restructuring temporal data, and so forth, [Gadia 1988, Tansel and Tin 1997]. There is a growing interest in temporal databases. A first book dedicated to temporal databases [Tansel at al 1993] followed by others addressing issues in handling time-varying data [Betini, Jajodia and Wang 1988, Date, Darwen and Lorentzos 2002, Snodgrass 1999].

scholarly journals Top-k Matching Queries for Filter-Based Profile Matching in Knowledge Bases

2017 ◽  
Author(s):  
Alejandra Lorena Paoletti ◽  
Jorge Martinez-Gil ◽  
Klaus-Dieter Schewe
Keyword(s):  
Human Resources ◽  
Partial Order ◽  
Relational Databases ◽  
Knowledge Bases ◽  
Efficient Design ◽  
Profile Matching ◽  
Job Offers ◽  
Query Algorithm ◽  
Single Relation ◽  
Matching Relation

Finding the best matching job offers for a candidate profile or, the best candidates profiles for a particular job offer, respectively constitutes the most common and most relevant type of queries in the Human Resources (HR) sector. This technically requires to investigate top-k queries on top of knowledge bases and relational databases. We propose in this paper a top-k query algorithm on relational databases able to produce effective and efficient results. The approach is to consider the partial order of matching relations between jobs and candidates profiles together with an efficient design of the data involved. In particular, the focus on a single relation, the matching relation, is crucial to achieve the expectations.

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