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.

MODELING AND MANAGEMENT OF TEMPORAL DATA IN OBJECT-ORIENTED KNOWLEDGE BASES

1998 ◽  
Vol 07 (03) ◽  
pp. 341-371 ◽  
Author(s):  
STANLEY Y. W. SU ◽  
HSIN-HSING M. CHEN
Keyword(s):  
Query Language ◽  
Strong Support ◽  
Object Oriented ◽  
Knowledge Bases ◽  
Data Partitioning ◽  
Temporal Data ◽  
Implementation Techniques ◽  
Temporal Object ◽  
Knowledge Rules ◽  
Temporal Algebra

There has been a considerable amount of work on object-oriented databases, active databases, and deductive databases. The common objective of these efforts is to produce highly intelligent and active systems for supporting the next generation of database applications. These future systems must be capable of capturing the concepts of time and managing not just temporal data but temporal knowledge expressed by knowledge rules. In this paper, we describe our efforts on a temporal object-oriented knowledge model, OSAM*/T, its associated temporal query language, OQL/T, an underlying temporal algebra, TA-algebra, and some implementation techniques. In addition to the features of the traditional object-oriented paradigm, the model is characterized by its strong support of association types and its incorporation of temporal knowledge rules for specifying temporal and other types of semantic constraints associated with object classes and their temporal object instances. The query language is featured by its pattern-based specification of temporal object associations, which allows complex queries with various time constraints to be formulated in a relatively simple way. The temporal algebra provides a set of primitive operators for manipulating homogeneous and/or heterogeneous patterns of temporal object associations, thus providing the needed mathematical foundation for processing and optimizing temporal queries. The implementation techniques include a Delta-Instance and Multi-Snapshot Storage Model, as well as data partitioning and clustering schemes for storage management of temporal knowledge bases.

Using the whole image and restrictions in the research of properties of some signature operations in Table Algebra

2019 ◽  
Vol 33 ◽  
pp. 112-121
Author(s):  
Nadiya Kakhuta ◽  
Alexey Senchenko
Keyword(s):  
Binary Relation ◽  
Relational Databases ◽  
Query Language ◽  
Relational Algebra ◽  
General Interest ◽  
Table Algebras ◽  
Modern Analogue ◽  
Table Data ◽  
Table Algebra ◽  
Composition Of Relations

The features of the whole image relative to many binary relation, and restrictions on a binary relation on the set for some of the signature operations of Table Algebra are used in the work. Constructions of the whole image and restrictions are of general interest for Mathematics, and Table Algebra is a modern analogue of Codd's well-known Relational Algebra. It forms the theoretical foundation of modern query language databases. Elements of the carrier of Table Algebra specify relational table data structures, and signature operations are based on the basic table manipulations in Relational Algebra and SQL-like languages. The following results in the research of the features of the whole image were obtained: interconnections between the whole image and restrictions were found; the monotony and distribution of the whole image and restrictions on unions, a criterion of their emptiness and interconnections with first and second projection relations were proved; the whole image of the composition of relations and composition restrictions were found; the distribution of restriction on intersection of sets was set; the estimates of the distribution of the whole image of intersection and difference of sets were given; criteria for distribution of the whole image relative to the intersection and differences of sets were found. In addition, the clues were provided with the help of the whole image and restrictions on some of the signature operations of Table Algebra: intersection, union, difference, projection and joining. These representations allowed us to obtain some features of these operations, which derive directly from the features of the whole image and restrictions. It is supposed to get similar views on other signature operations of Table Algebras and to allocate their features arising from such representation. The obtained results can be used in the theory of Table Algebra as an approach to the research of the features of their signature operations, this can be used in query optimization in relational databases.

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].

scholarly journals Temporal Query Answering in DL-Lite with Negation

10.29007/2df8 ◽  
2018 ◽  
Author(s):  
Stefan Borgwardt ◽  
Veronika Thost
Keyword(s):  
Temporal Logic ◽  
Domain Knowledge ◽  
Relational Databases ◽  
Query Language ◽  
Description Logics ◽  
Query Answering ◽  
Open World ◽  
Temporal Query ◽  
Temporal Query Language ◽  
Open World Assumption

Ontology-based query answering augments classical query answering in databases by adopting the open-world assumption and by including domain knowledge provided by an ontology. We investigate temporal query answering w.r.t. ontologies formulated in DL-Lite, a family of description logics that captures the conceptual features of relational databases and was tailored for efficient query answering. We consider a recently proposed temporal query language that combines conjunctive queries with the operators of propositional linear temporal logic (LTL). In particular, we consider negation in the ontology and query language, and study both data and combined complexity of query entailment.

The End of Relational Databases Domination

2018 ◽  
pp. 1-33
Keyword(s):  
Relational Databases ◽  
Query Language ◽  
Nosql Databases ◽  
Database Development ◽  
Relational Data Model ◽  
Durability Properties ◽  
Nosql Database ◽  
Soft State ◽  
Database Models ◽  
Consistent Approach

The chapter gives an overview of the three main stages of database development: hierarchical and network database models, relational database model, and NoSQL databases. It gives a short overview of the pillars of relational databases: relational data model, ACID (atomicity, consistency, isolation, and durability) properties of a transaction, and SQL (structured query language). Also, the concepts that make the base for NoSQL database development are explained, including the CAP (Consistency, Availability, Partitioning) theorem, the BASE (Basically Available, Soft-state, Eventually consistent) approach, and the sharding phenomenon. At last, the limitations of relational databases which led to the development of NoSQL databases are discussed.

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 Practical Querying of Temporal Data via OWL 2 QL and SQL:2011

10.29007/rlv9 ◽  
2018 ◽  
Author(s):  
Szymon Klarman
Keyword(s):  
Temporal Logic ◽  
Query Language ◽  
Practical Approach ◽  
Data Complexity ◽  
Temporal Data ◽  
First Order ◽  
Temporal Query ◽  
Temporal Query Language ◽  
Formal Properties ◽  
Semantic Layer

We develop a practical approach to querying temporal data stored in temporal SQL:2011 databases through the semantic layer of OWL 2 QL ontologies. An interval-based temporal query language (TQL), which we propose for this task, is defined via naturally characterizable combinations of temporal logic with conjunctive queries. This foundation warrants well-defined semantics and formal properties of TQL querying. In particular, we show that under certain mild restrictions the data complexity of query answering remains in AC$^0$, i.e., as in the usual, nontemporal case. On the practical side, TQL is tailored specifically to offer maximum expressivity while preserving the possibility of reusing standard first-order rewriting techniques and tools for OWL 2 QL.

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).

GuessXQ

2013 ◽  
pp. 57-76
Author(s):  
Daniela Morais Fonte ◽  
Daniela da Cruz ◽  
Pedro Rangel Henriques ◽  
Alda Lopes Gancarski
Keyword(s):  
Information Retrieval ◽  
Learning Process ◽  
Web Application ◽  
Relational Databases ◽  
Query Language ◽  
General Purpose ◽  
Considerable Effort ◽  
Document Structure ◽  
Query By Example ◽  
Xml Documents

XML is a widely used general-purpose annotation formalism for creating custom markup languages. XML annotations give structure to plain documents to interpret their content. To extract information from XML documents XPath and XQuery languages can be used. However, the learning of these dialects requires a considerable effort. In this context, the traditional Query-By-Example methodology (for Relational Databases) can be an important contribution to leverage this learning process, freeing the user from knowing the specific query language details or even the document structure. This chapter describes how to apply the Query-By-Example concept in a Web-application for information retrieval from XML documents, the GuessXQ system. This engine is capable of deducing, from an example, the respective XQuery statement. The example consists of marking the desired components directly on a sample document, picked-up from a collection. After inferring the corresponding query, GuessXQ applies it to the collection to obtain the desired result.

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