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Outline

Title
Abstract
Introduction
The Temporal Data Model
T_Create TS (Valid Time, Data)
T_Update TS (Valid Time, Data)
T_Correct TS (Valid Time, Data)
Conclusions and Outlook
References
All Topics
Mathematics
Computational Mathematics

Support for Temporal Data by Complex Objects

Profile image of Harald SchöningHarald Schöning

1990, Very Large Data Bases

Abstract

Support for temporal data continues to be a requirement posed by many applications. We show that a complex object data model is an appropriate means for handling temporal data. Firstly, we describe the main features of temporal databases in terms of time sequences, valid time, etc. We then explain the mapping of time sequences onto recursively structured complex objects. Operations on temporal data are easily transformed into complex object operations. To cope with the huge storage requirements arising from temporal databases, we integrate the concept of storing logical differences into our approach. Here, we exploit the extensibility of the underlying complex object's database system PRIMA. Finally, we briefly sketch a further improvement to guarantee fast access to the present data by storing them apart from the historical data without loosing the connection between both.

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Resolution of time concepts in temporal databases
Seung-Kyum Kim

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In this paper we identify and present three distinct time concepts for precise and lossless information preservation in temporal databases. We provide a formal definition of temporal validity and further extend it to the notion of interpretation-based validity, with which the confusion among various time concepts introduced earlier for temporal databases is resolved. Then, we discuss the problem of preserving multiple past states of a temporal database, which leads to the identification of a maximal set of time concepts. It is shown that the time concept event time is needed to correctly model retroactive and proactive updates, as it is not possible to model them using only the valid and transaction times as thought earlier. We also show the adequacy of three time concepts (event time, along with valid and transaction times) for completely preserving different past states generated by retroactive and proactive updates, error corrections, and delayed updates. In addition, we define the evolution of an object (i.e., object's history) along a single time dimension (valid time) by using temporal and interpretation-based validity. Finally, we sketch the implementation of history for the relational data model. 'Note that the effective time and registration time, similar to the logical time and physical time, respectively, were proposed earlier in [4].

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Complex time management in databases
Karol Matiaško

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Temporal database is an extension of the concept of standard databases which process only current valid data. Temporal structure is not based only on managing historical data, but it should also model the data, the validity of which will be in the future in special structures. This paper deals with the temporal structure on object level in comparison with the column level temporal data. It describes the principles, required methods, procedures, functions and triggers to provide the functionality of this system. It also defines the possible implementations and offers the solution to get the snapshot of the database or the object whenever during the existence. The reason for column level solution development is based on the heterogeneity of the attributes time. Some attributes, however, do not change their values over the time or are updated very rarely, and therefore it is not necessary to record the new values for these attributes.

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References (44)

  1. References Ar86: Ariav, G.: A Temporally Oriented Data Model, ACM TODS, Vol. 11, No. 4, 1986, pp. 499-527.
  2. AS85: Ahn, I., Snodgrass, R.: A Taxonomy of Time in Databases, Proc. ACM SIGMOD Int. Conf. on Management of Data, Aus- tin, 1985, pp. 236-246.
  3. AS86a: Ahn, I., Snodgrass, R.: Temporal Databases, IEEE COMPUT- ER, Vol. 19, No. 9, 1986, pp. 35-42.
  4. AS86b: Ahn, I., Snodgrass, R.: Performance Evaluation of a Temporal Database Management System, Proc. ACM SIGMOD Int. Conf. on Management of Data, Washington, 1986, pp. 96-107.
  5. AS88: Ahn, I., Snodgrass, R.: Partitioned Storage for Temporal Data- bases, Information Systems, Vol. 13, No. 4, 1988, pp. 369-391.
  6. Bo82: Bolour, A., Anderson, T.-L., Dekeyser, L.-J., Wong, H.-K.-T.: The Role of Time in Information Processing: A Survey, ACM SIGMOD RECORD, Vol. 12, No. 3, 1982, pp. 27-50.
  7. CW83: Clifford, J., Warren, D.-S.: Formal Semantics for Time in Da- tabases, ACM TODS, Vol. 8, No. 2, 1983, pp. 214-254.
  8. Da86: Dadam, P., et al.: A DBMS Prototype to Support Extended NF2 Relations: An Integrated View on Flat tables and Hierarchies, Proc. ACM SIGMOD Int. Conf. on Management of Data, Washington, 1986, pp. 356-367.
  9. DLW84: Dadam, P., Lum, V., Werner, H.-D.: Integration of Time Ver- sions into a Relational Database System, Proc. 10th Int. Conf. on VLDB, Singapore, 1984, pp. 509-522.
  10. Ga88: Gadia, S.-K.: A Homogeneous Relational Model and Query Language for Temporal Databases, ACM TODS, Vol. 13, No. 4, Dec. 1988, pp. 418-448.
  11. Hä88 Härder, T.: Overview of the PRIMA Project, in: Härder, T. ( ed.): The PRIMA Project -Design and Implementation of a Non-Standard Database System, Research Report No. 26/88, University Kaiserslautern, 1988, pp. 1-12.
  12. Kä90: Käfer, W.: The Temporal Query Language TMQL, Internal Re- port, University Kaiserslautern, 1990.
  13. Kl81: Klopprogge, M.-R.: TERM An Approach to Include the Time Dimension in the Entity-Relationship Model, Proc. 2nd Int. Conf. on Entity-Relationship Approach, 1981, pp. 477-512. Lu84: Lum, V., Dadam, P., Erbe, R., Guenauer, J., Pistor, P., Walch, G., Werner, H., Woodfill, J.: Designing DBMS Support for the Temporal Dimension, Proc. ACM SIGMOD Int. Conf. on Management of Data, Boston, 1984, pp. 115-130.
  14. Mi88 Mitschang, B.: Towards a Unified View of Design Data and Knowledge Representation, Proc. 2nd Int. Conf. on Expert Da- tabase Systems, Tysons Corner, 1988, pp. 33-50.
  15. Mi89 Mitschang, B.: Extending the Relational Algebra to Capture Complex Objects, Proc. 15th Int. Conf. on VLDB, Amsterdam, 1989, pp. 297-305.
  16. RS87: Rotem, D., Segev, A.: Physical Organization of Temporal Data, Proc. Int. Conf. on Data Engineering, IEEE Computer Society, Los Angeles, 1987, pp. 547-553.
  17. Schö89: Schöning, H.: Integrating Complex Objects and Recursion, Proc. of the 1st Int. Conf. on Deductive and Object-Oriented Database Systems, Kyoto, Japan, 1989, pp. 535-554.
  18. SK86: Shoshani, A., Kawagoe, K.: Temporal Data Management, Proc. 12th Int. Conf. on VLDB, Kyoto, Japan, 1986, pp. 79-88.
  19. Sn86: Snodgrass, R.: Research Concerning Time in Databases: Project Summaries, ACM SIGMOD RECORD, Vol. 15, No. 4, 1986, pp. 19-39.
  20. Sn87: Snodgrass, R.: The Temporal Query Language TQuel, ACM TODS, Vol. 12, No. 2, 1987, pp. 247-298.
  21. SS86: Schek, H.-J., Scholl, M.-H.: The Relational Model with Rela- tion Valued Attributes, Information Systems, Vol. 11, No. 4, 1986, pp. 137-148.
  22. SS90: Schöning, H., Sikeler, A.: Extending and Configuring the PRI- MA Database Management System Kernel (extended abstract), in: Proc. PARBASE-90, Miami Beach, Florida, 1990. Ta86: Tansel, A.-U.: Adding Time Dimension to Relational Model and Extending Relational Algebra, Information Systems, Vol. 11, No. 4, 1986, pp. 343-355.
  23. References Ar86: Ariav, G.: A Temporally Oriented Data Model, ACM TODS, Vol. 11, No. 4, 1986, pp. 499-527.
  24. AS85: Ahn, I., Snodgrass, R.: A Taxonomy of Time in Databases, Proc. ACM SIGMOD Int. Conf. on Management of Data, Aus- tin, 1985, pp. 236-246.
  25. AS86a: Ahn, I., Snodgrass, R.: Temporal Databases, IEEE COMPUT- ER, Vol. 19, No. 9, 1986, pp. 35-42.
  26. AS86b: Ahn, I., Snodgrass, R.: Performance Evaluation of a Temporal Database Management System, Proc. ACM SIGMOD Int. Conf. on Management of Data, Washington, 1986, pp. 96-107.
  27. AS88: Ahn, I., Snodgrass, R.: Partitioned Storage for Temporal Data- bases, Information Systems, Vol. 13, No. 4, 1988, pp. 369-391.
  28. Bo82: Bolour, A., Anderson, T.-L., Dekeyser, L.-J., Wong, H.-K.-T.: The Role of Time in Information Processing: A Survey, ACM SIGMOD RECORD, Vol. 12, No. 3, 1982, pp. 27-50.
  29. CW83: Clifford, J., Warren, D.-S.: Formal Semantics for Time in Da- tabases, ACM TODS, Vol. 8, No. 2, 1983, pp. 214-254.
  30. Da86: Dadam, P., et al.: A DBMS Prototype to Support Extended NF2 Relations: An Integrated View on Flat tables and Hierarchies, Proc. ACM SIGMOD Int. Conf. on Management of Data, Washington, 1986, pp. 356-367.
  31. DLW84: Dadam, P., Lum, V., Werner, H.-D.: Integration of Time Ver- sions into a Relational Database System, Proc. 10th Int. Conf. on VLDB, Singapore, 1984, pp. 509-522.
  32. Ga88: Gadia, S.-K.: A Homogeneous Relational Model and Query Language for Temporal Databases, ACM TODS, Vol. 13, No. 4, Dec. 1988, pp. 418-448.
  33. Hä88 Härder, T.: Overview of the PRIMA Project, in: Härder, T. ( ed.): The PRIMA Project -Design and Implementation of a Non-Standard Database System, Research Report No. 26/88, University Kaiserslautern, 1988, pp. 1-12.
  34. Kä90: Käfer, W.: The Temporal Query Language TMQL, Internal Re- port, University Kaiserslautern, 1990.
  35. Kl81: Klopprogge, M.-R.: TERM An Approach to Include the Time Dimension in the Entity-Relationship Model, Proc. 2nd Int. Conf. on Entity-Relationship Approach, 1981, pp. 477-512. Lu84: Lum, V., Dadam, P., Erbe, R., Guenauer, J., Pistor, P., Walch, G., Werner, H., Woodfill, J.: Designing DBMS Support for the Temporal Dimension, Proc. ACM SIGMOD Int. Conf. on Management of Data, Boston, 1984, pp. 115-130.
  36. Mi88 Mitschang, B.: Towards a Unified View of Design Data and Knowledge Representation, Proc. 2nd Int. Conf. on Expert Da- tabase Systems, Tysons Corner, 1988, pp. 33-50.
  37. Mi89 Mitschang, B.: Extending the Relational Algebra to Capture Complex Objects, Proc. 15th Int. Conf. on VLDB, Amsterdam, 1989, pp. 297-305.
  38. RS87: Rotem, D., Segev, A.: Physical Organization of Temporal Data, Proc. Int. Conf. on Data Engineering, IEEE Computer Society, Los Angeles, 1987, pp. 547-553.
  39. Schö89: Schöning, H.: Integrating Complex Objects and Recursion, Proc. of the 1st Int. Conf. on Deductive and Object-Oriented Database Systems, Kyoto, Japan, 1989, pp. 535-554.
  40. SK86: Shoshani, A., Kawagoe, K.: Temporal Data Management, Proc. 12th Int. Conf. on VLDB, Kyoto, Japan, 1986, pp. 79-88.
  41. Sn86: Snodgrass, R.: Research Concerning Time in Databases: Project Summaries, ACM SIGMOD RECORD, Vol. 15, No. 4, 1986, pp. 19-39.
  42. Sn87: Snodgrass, R.: The Temporal Query Language TQuel, ACM TODS, Vol. 12, No. 2, 1987, pp. 247-298.
  43. SS86: Schek, H.-J., Scholl, M.-H.: The Relational Model with Rela- tion Valued Attributes, Information Systems, Vol. 11, No. 4, 1986, pp. 137-148.
  44. SS90: Schöning, H., Sikeler, A.: Extending and Configuring the PRI- MA Database Management System Kernel (extended abstract), in: Proc. PARBASE-90, Miami Beach, Florida, 1990. Ta86: Tansel, A.-U.: Adding Time Dimension to Relational Model and Extending Relational Algebra, Information Systems, Vol. 11, No. 4, 1986, pp. 343-355.

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Support for temporal data continues to be a requirement posed by many applications such as VLSI design and CAD, but also in con- ventional applications like banking and sales. Furthermore, the strong demand for complex-object support is known as an inherent fact in design applications, and also emerges for advanced "conven- tional" applications. Thus, new advanced database management systems should

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Temporal Queries to Complex Objects
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Support for temporal data continues to be a requirement posed by many applications such as VLSI design and CAD, but also in conventional applications like banking and sales. The strong demand for complex-object support is known as an inherent fact in design applications, but it also holds for advanced "conventional" applications. Thus, new advanced database management systems should include both features, i.e. should support temporal complex-objects. We show that such a system can be efficiently implemented on top of a (non-temporal) complex-object data model. The central notion of the temporal complex-object data model is a time slice, representing one state of a complex object. Time slices cannot be directly stored, if non-disjunct (i.e. overlapping) complex objects are allowed. We explain the mapping of time slices onto the complex objects supported by the MAD model. Operations on temporal complexobjects are easily transformed into MAD model operations. Furthermore, we reduce the huge storage requirements usually arising from temporal databases.

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Approaches to Handling Temporal Data in Object-Oriented Databases
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Abstract Temporal databases are an active and fast growing research area. Although many temporal extensions of the relational data model have been proposed, there is no comparable amount of work in the context of object-oriented data models. Moreover, few of the proposed models have been implemented. This report discusses how the temporal data model developed for T Chimera 4] has been implemented on top of the Ode OODBMS 1]. In particular, two di erent approaches to handle temporal data are investigated.

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Temporal Data Management
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In this paper we develop a framework for the sup- port of temporal data. The concept of a time se- quence is introduced, and shown to be an important fundamental concept for representing the semantics of temporal data and for efficient physical organization. We discuss properties of time sequences that allow the treatment of such sequences in a uniform fash-

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In previous work, we introduced a data model and a query language for temporal data. The model was designed independently of any existing data model rather than an extension of one. This approach provided an insight into the special requirements for handling temporal data. In this paper, we discuss the implications of supporting such a model in the relational database

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Related topics

  • Computer Science
  • Structural Complexity
  • Temporal Logic
  • Very Large Data Bases
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