scholarly journals Modeling and Querying Temporal Data

2005 ◽  
pp. 373-377 ◽  
Author(s):  
Abdullah Uz Tansel
Keyword(s):  
Data Model ◽  
Current Data ◽  
Relational Data ◽  
Temporal Data ◽  
Temporal Database ◽  
Time Dimension ◽  
Organizational Intelligence ◽  
Relational Data Model ◽  
General Store ◽  
Future Data

Databases in general 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 has a time dimension and maintains time-varying data (i.e., past, present, and future data). In this article, we focus on the relational data model and address the subtle issues in modeling temporal data, such as comparing database states at two different time points, capturing the periods for concurrent events, and accessing to times beyond these periods, handling multivalued attributes, coalescing, and restructuring temporal data (Gadia 1988, Tansel & Tin, 1997). Many extensions to the relational data model have been proposed for handling temporal data.

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].

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 Storing Hypergraph-Based Data Models in Non-hypergraph Data Storage and Applications for Information Systems

2021 ◽  
pp. 1-21
Author(s):  
Bálint Molnár ◽  
András Béleczki ◽  
Bence Sarkadi-Nagy
Keyword(s):  
Information Systems ◽  
Data Storage ◽  
Data Model ◽  
Relational Data ◽  
Complex Data ◽  
Graph Representations ◽  
Relational Data Model ◽  
Rich Information ◽  
The Relationship

Data structures and especially the relationship among the data entities have changed in the last couple of years. The network-like graph representations of data-model are becoming more and more common nowadays, since they are more suitable to depict these, than the well-established relational data-model. The graphs can describe large and complex networks — like social networks — but also capable of storing rich information about complex data. This was mostly of relational data-model trait before. This also can be achieved with the use of the knowledge representation tool called “hypergraphs”. To utilize the possibilities of this model, we need a practical way to store and process hypergraphs. In this paper, we propose a way by which we can store hypergraphs model in the SAP HANA in-memory database system which has a “Graph Core” engine besides the relational data model. Graph Core has many graph algorithms by default however it is not capable to store or to work with hypergraphs neither are any of these algorithms specifically tailored for hypergraphs either. Hence in this paper, besides the case study of the two information systems, we also propose pseudo-code level algorithms to accommodate hypergraph semantics to process our IS model.

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