Treatment of History in Temporal Objects

2016

All the variable recorded information in the form of raw data which further manipulated and defined in such a way which is meaningful and correlated. These information were recorded at some point in time which may or may not acknowledge the importance of the time at which it has been initiated, processed and terminated. There are uncountable number of databases like medical histories, banking transactions, employee’s database, etc., which are being maintained ever since the mankind evolved and started to keep the record of such information. Time is the key factor to any database created irrespective of its attributes and the values that are being stored in it. This article is consist of the details about the temporal database (TD) and the concepts that shows the functionalities and the importance of the TD in the real world. This article also explains that how the time can further be dissected into smaller versions or granules which has its own significance. Therefore, the main focu...

Time is one of the most difficult aspects to handle in real world applications, especially in database systems. Relational database management systems proposed by Codd offer very little built-in support for managing time varying data and theory of temporal semantics. Many temporal extensions of the relational model have been proposed and some of them are also implemented. The proposed system is based on an ontology of health Care terminology including patients vital data, activities and as well as the treatment data. We will propose a conceptual temporal model for handling time varying attributes in the relational database model with minimal temporal attributes. The proposed model is easy to define, manage and incorporates the important and relevant features in the target temporal relational model. Furthermore we have illustrated implementation of the model on patient database and also present the requirement analysis: work flow for managing patients in hospital environment. In response to rising health care costs, reliability, privacy, security and changing expectations concerning the quality of health care, data management is very important in improving health care services.

2003

Physicians and medical decision-support applications, such as for diagnosis, therapy, monitoring, quality assessment, and clinical research, reason about patients in terms of abstract, clinically meaningful concepts, typically over significant time periods. Clinical databases, however, store only raw, time-stamped data. Thus, there is a need to bridge this gap. We introduce the Temporal Abstraction Language (TAR) which enables specification of abstract relations involving raw data and abstract concepts, and use it for defining typical medical abstraction patterns. For each pattern we further analyze finiteness properties of the answer set. The TAR language is implemented as the reasoning module in a practical diagnosis system. Index Terms-temporal reasoning, temporal databases, temporal query languages, knowledge-based systems and knowledge representation, medical informatics and temporal abstraction.

2010

2004

The paper “Proposed Temporal Database Concepts— May 1993” contained a complete set of glossary entries proposed by members of the temporal database community from Spring 1992 until May 1993. The aim of the proposal was to define a consensus glossary of temporal database concepts and names. Several glossary entries (Section 3) were included in the proposal, but were still unresolved at the time of the deadline. This addendum reflects on-going discussions and contains revised versions of several unresolved entries. The entries here thus supersede the corresponding entries in Section 3 of the proposal.

Journal of the American Medical Informatics Association, 2000

A b s t r a c t Most health care databases include time-stamped instant data as the only temporal representation of patient information. Many previous efforts have attempted to provide frameworks in which medical databases could be queried in relation to time. These, however, have required either a sophisticated database representation of time, including time intervals, or a time-stamp-based database coupled with a nonstandard temporal query language. In this work, the authors demonstrate how their previously described data retrieval application, DXtractor, can be used as a database querying application with expressive power close to that of temporal databases and temporal query languages, using only standard SQL and existing timestamp-based repositories. DXtractor provides the ability to compose temporal queries through an interface that is understood by nonprogramming medical personnel. Not all temporal constructs are easily implemented using this framework; nonetheless, DXtractor's temporal capabilities provide a significant improvement in the temporal expressivity accessible to clinicians using standard time-stamped clinical databases.

Temporal Databases: Research and Practice, 1998

Abstract. This document 1 contains definitions of a wide range of concepts specific to and widely used within temporal databases. In addition to providing definitions, the document also includes explanations of concepts as well as discussions of the adopted names. The consensus effort that lead to this glossary was initiated in Early 1992. Earlier versions appeared in SIGMOD Record in September 1992 and March 1994. The present glossary subsumes all the previous documents. The glossary meets the need for creating a higher degree of consensus on ...

IEEE Transactions on Information Technology in Biomedicine, 1997

The need to manage temporal information given at different levels of granularity or with indeterminacy is common to many application areas. Among them, we focus on clinical data management. Different time granularities and indeterminacy are also needed in querying temporal databases. In this paper, we describe GCH-OSQL (Granular Clinical History-Object Structured Query Language), an object-oriented/temporally-oriented extension of SQL. GCH-OSQL is based on an object-oriented temporal data model, GCH-OODM. GCH-OODM allows storage of clinical information at different and mixed granularities or with temporal indeterminacy. GCH-OSQL deals with the valid time of clinical information. The temporal extension of the SELECT construct includes the addition of the TIME-SLICE and MOVING WINDOW clauses, and the capability to reference the temporal dimension of objects in the WHERE and SELECT clauses. Using object-oriented technologies, a system prototype for GCH-OSQL and GCH-OODM has been implemented and applied to data management of follow-up patients after coronary angioplasty intervention.

2003

Physicians and medical decision-support applications, such as for diagnosis, therapy, monitoring, quality assessment, and clinical research, reason about patients in terms of abstract, clinically meaningful concepts, typically over significant time periods. Clinical databases, however, store only raw, time-stamped data. Thus, there is a need to bridge this gap. We introduce the Temporal Abstraction Language (TAR) which enables specification of abstract relations involving raw data and abstract concepts, and use it for defining typical medical abstraction patterns.

1998

Time is important in clinical information systems. Representing, maintaining, querying, and reasoning about time-oriented clinical data is a major theoretical and practical research area in medical informatics. In this nonexhaustive overview, we present a brief synopsis of research efforts in designing and developing time-oriented information systems in medicine. These efforts can be viewed from either an application point of view, distinguishing between different clinical tasks (such as diagnosis versus therapy) and clinical areas (such as infectious diseases versus oncology), or a methodological point of view, distinguishing between different theoretical approaches. We also explore the two primary methodological and theoretical paths research has taken in the past decade: temporal reasoning and temporal data maintenance. Both of these research areas include efforts to model time, temporal entities, and temporal queries. Collaboration between the two areas is possible, through tasks such as the abstraction of raw time-oriented clinical data into higher-level meaningful clinical concepts and the management of different levels of temporal granularity. Such collaboration could provide a common ground and useful areas for future research and development. We conclude with our view of future research directions.