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Richard T Snodgrass - One of the best experts on this subject based on the ideXlab platform.
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unifying temporal Data models via a conceptual model
Information Systems, 1994Co-Authors: Christia S Jense, Michael D Soo, Richard T SnodgrassAbstract:Abstract To add time support to the relational model, both first normal form (1NF) and non-1NF Data models have been proposed. Each has associated advantages and disadvantages. For example, remaining within 1NF when time support is added may introduce Data redundancy. On the other hand, well-established storage organization and query evaluation techniques require atomic attribute values, and are thus intended for 1NF models; utilizing a non-1NF model may degrade performance. This paper describes a new temporal Data model designed with the single purpose of capturing the time-dependent semantics of Data. Here, tuples of Bitemporal relations are stamped with sets of two-dimensional chronons in transaction-time/valid-time space. We use the notion of snapshot equivalence to map temporal relation instances and temporal operators of one existing model to equivalent instances and operators of another. We examine five previously proposed schemes for representing Bitemporal Data: two are tuple-timestamped 1NF representations, one is a Backlog relation composed of 1NF timestamped change requests, and two are non-1NF attribute value-timestamped representations. The mappings between these models are possible using mappings to and from the new conceptual model. The framework of well-behaved mappings between models, with the new conceptual model at the center, illustrates how it is possible to use different models for display and storage purposes in a temporal Database system. Some models provide rich structure and are useful for display of temporal Data, while other models provide regular structure useful for storing temporal Data. The equivalence mappings effectively move the distinction between the investigated Data models from a semantic basis to a display-related or a physical, performance-relevant basis, thereby allowing the exploitation of different Data models by using each for the task(s) for which they are best suited.
Christia S Jense - One of the best experts on this subject based on the ideXlab platform.
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unifying temporal Data models via a conceptual model
Information Systems, 1994Co-Authors: Christia S Jense, Michael D Soo, Richard T SnodgrassAbstract:Abstract To add time support to the relational model, both first normal form (1NF) and non-1NF Data models have been proposed. Each has associated advantages and disadvantages. For example, remaining within 1NF when time support is added may introduce Data redundancy. On the other hand, well-established storage organization and query evaluation techniques require atomic attribute values, and are thus intended for 1NF models; utilizing a non-1NF model may degrade performance. This paper describes a new temporal Data model designed with the single purpose of capturing the time-dependent semantics of Data. Here, tuples of Bitemporal relations are stamped with sets of two-dimensional chronons in transaction-time/valid-time space. We use the notion of snapshot equivalence to map temporal relation instances and temporal operators of one existing model to equivalent instances and operators of another. We examine five previously proposed schemes for representing Bitemporal Data: two are tuple-timestamped 1NF representations, one is a Backlog relation composed of 1NF timestamped change requests, and two are non-1NF attribute value-timestamped representations. The mappings between these models are possible using mappings to and from the new conceptual model. The framework of well-behaved mappings between models, with the new conceptual model at the center, illustrates how it is possible to use different models for display and storage purposes in a temporal Database system. Some models provide rich structure and are useful for display of temporal Data, while other models provide regular structure useful for storing temporal Data. The equivalence mappings effectively move the distinction between the investigated Data models from a semantic basis to a display-related or a physical, performance-relevant basis, thereby allowing the exploitation of different Data models by using each for the task(s) for which they are best suited.
Michael D Soo - One of the best experts on this subject based on the ideXlab platform.
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unifying temporal Data models via a conceptual model
Information Systems, 1994Co-Authors: Christia S Jense, Michael D Soo, Richard T SnodgrassAbstract:Abstract To add time support to the relational model, both first normal form (1NF) and non-1NF Data models have been proposed. Each has associated advantages and disadvantages. For example, remaining within 1NF when time support is added may introduce Data redundancy. On the other hand, well-established storage organization and query evaluation techniques require atomic attribute values, and are thus intended for 1NF models; utilizing a non-1NF model may degrade performance. This paper describes a new temporal Data model designed with the single purpose of capturing the time-dependent semantics of Data. Here, tuples of Bitemporal relations are stamped with sets of two-dimensional chronons in transaction-time/valid-time space. We use the notion of snapshot equivalence to map temporal relation instances and temporal operators of one existing model to equivalent instances and operators of another. We examine five previously proposed schemes for representing Bitemporal Data: two are tuple-timestamped 1NF representations, one is a Backlog relation composed of 1NF timestamped change requests, and two are non-1NF attribute value-timestamped representations. The mappings between these models are possible using mappings to and from the new conceptual model. The framework of well-behaved mappings between models, with the new conceptual model at the center, illustrates how it is possible to use different models for display and storage purposes in a temporal Database system. Some models provide rich structure and are useful for display of temporal Data, while other models provide regular structure useful for storing temporal Data. The equivalence mappings effectively move the distinction between the investigated Data models from a semantic basis to a display-related or a physical, performance-relevant basis, thereby allowing the exploitation of different Data models by using each for the task(s) for which they are best suited.
Mahbooba Asra - One of the best experts on this subject based on the ideXlab platform.
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Zonal-Level Urban Sprawl Analysis using Digitally-Merged Resourcesat-LISS IV and Cartosat-PAN Bitemporal Data
International Journal of Advanced Remote Sensing and GIS, 2015Co-Authors: C R Prakash, B. Sreedevi, K Y Kishore, J. Venkatesh, Ravi Shankar Dwivedi, Mahbooba AsraAbstract:Remote sensing and GIS along with collateral Data help analyzing the growth, pattern and extent of urban sprawl. With such a spatial and temporal analyses, it is possible to identify the pattern of sprawl and subsequently predict the nature of future expansion. The article brings out the extent and spatial distribution of urban sprawl over a period of six years i.e. 2005-2011 using Resourcesat-1 LISS-IV and Cartosat-1 PAN Data over Hyderabad metropolitan city, Telangana state, India. The approach comprises Data preparation-radiometric normalization, geo-referencing and image fusion; on-screen visual interpretation, and change analysis in a GIS environment. The study reveals that the built-up land have expanded by 8.65% during the 6-year period. Furthermore, in terms of growth, the high density built-up land score over their low density counterpart (5.7% versus 2.96%). Such a growth could happen at the cost of scrubs, cropland and barren/rocky area to a great extent and at the expanse of water bodies to a lesser extent. An estimated 65.294 sqkm of scrubs, 40.319 sqkm of cropland and 14.523 sqkm of barren/rocky areas have been transformed into settlements. Data used, methodology employed and the results of the study are discussed in detail.
Randall Weis - One of the best experts on this subject based on the ideXlab platform.
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a brief history of temporal Data management
Managing Time in Relational Databases#R##N#How to Design Update and Query Temporal Data, 2010Co-Authors: Tom Johnston, Randall WeisAbstract:This chapter presents a brief history of temporal Data management, and discusses a brief account of changes in methods of managing Data over the last quarter-century. Temporal Data management is not a new development. From the earliest days of business Data processing (as it was called back then), transactions were captured in a transaction log and the files and tables those transactions were applied to were periodically backed up. With those backups and logfiles, one could usually recreate what the Data looked like at any point in time along either of the temporal dimensions we will be discussing. Indeed, together they contain all the “raw material” needed to support fully Bitemporal Data management. What has changed about temporal Data management, over the decades, is accessibility to temporal Data. In the 80s, as disk storage costs plummeted, the concept of backup Data introduced temporal Data management at the Database level. On an architecturally smaller scale, IT developers were also beginning to design and implement several other ways of managing temporal Data, such as the use of history tables, and the use of version tables. In addition, developers were also beginning to create on-line transaction tables. By the early 90s, significant computer science research on Bitemporality had been completed. The second major development in the 90s was that the concept of a Data warehouse was proselytized and extended by Bill Inmon. At about the same time, Ralph Kimball took a complementary approach, describing a method of recording history by means of a collection of transactions. In the first decade of 2000s, several major developments took place related to the management of temporal Data: on-line analytical processing (OLAP) Data cubes; slowly changing dimensions (SCDs); and real-time Data warehousing.
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Deferred Assertions and Other Pipeline Datasets
Managing Time in Relational Databases, 2010Co-Authors: Tom Johnston, Randall WeisAbstract:This chapter discusses the topic of pipeline Datasets, in general, and of one kind of pipeline Dataset—deferred assertions—in particular. It begins by noting that deferred assertions represent past, present, and future versions in future assertion time, but that past, present, and future versions also exist in past and present assertion time. This gives nine categories of temporal Data, one of which is currently asserted current versions of things, which known as conventional Data, physically located in production tables. The other eight categories correspond to pipeline Datasets, being Data that has those production tables as either their destinations or their origins. Deferred assertions are the result of applying deferred transactions to the Database. Instead of holding on to maintenance transactions until it is the right time to apply them, Asserted Versioning applies them right away, but does not immediately assert them. These deferred assertions may be updated or deleted by themselves. Just as deferred assertions replace collections of transactions that have not yet been applied to the Database, Bitemporal Data in any of the other seven categories replaces other physically external Datasets. Asserted version tables contain Data in all these temporal categories and, in doing so, internalize what would otherwise be physically distinct Datasets.