C. S. Jensen and R. T. Snodgrass. Temporal Data Management. IEEE TKDE, 11(1): 36--45 (1999).  Home/Search   Document Details and Download   Summary   Related Articles   Check

....over time. We assume the First Temporal Normal Form [22] which implies that no two records exist in a given temporal relation that have equal keys and intersecting intervals. A record with time interval i is called alive for all time instants in i. Moreover, we assume the transaction time model [11] which implies that record updates arrive in increasing time order. When a data record is inserted in a relation at time t, the end time of its interval is yet unknown and is thus initiated to now (a variable representing the ever increasing current time) Record deletions are logical, i.e. ....

C. Jensen and R. Snodgrass, "Temporal Data Management ", TKDE 11(1), 1999.

.... showed that the basic syntactic constructs and semantic notions provided by current query languages are sufficient if user defined functions and user defined aggregates are supported [24] With these minimal extensions, SQL sz can express queries as powerful as those expressible in other works [14, 10] in a simple and intuitive fashion. Efficient support for spario temporal queries can be obtained by using internal representations that are different from the conceptual one, and then mapping conceptual queries into equivalent queries on the internal representations. This approach has already ....

C. S. Jensen and R. T Shodgrass. Temporal Data Management. In IEEE Transactions on Knowledge and Data Engineering, Vol.11, No.1, pp.36-44, 1999

....databases provide support for past, current, or even future data and allow sophisticated queries over time to be stated [17] Research in temporal databases [5] has grown immensely in recent years. In particular, transaction time and valid time have been proposed [7] and investigated in detail [8], 15] In the field of data warehousing, Bliujute et al. in [1] concentrated on the shortcomings of star schemas in the context of slowly changing dimensions [9] and concluded that state oriented warehouses allow easier analytical processing and even better query performance than observed in ....

....past, and a series of snapshots can provide a view of the history of an organization. The standard approach to storing periodic data (typically found in DWHs) is to use time stamped status and event records. There are, however, a variety of schemes to maximize the efficiency of timestamps [2] [8], 15] The single timestamp approach storing only a start time when a record became valid, is well applicable to event data, but faces serious deficiencies in the context of DWHs, where in general state information is stored. There are two relatively common types of queries used in DWH ....

C.S. Jensen and R.T. Snodgrass. Temporal Data Management. In IEEE Transactions on Knowledge and Data Engineering, Vol. 11(1): pp. 36-44, 1999.

....Temporal databases provide support for past, current, or even future data and allow sophisticated queries over time to be stated [6, 19] Research in temporal databases has grown immensely in recent years. In particular, transaction time and valid time have been proposed [9] and investigated [10, 15]. In the field of data warehousing [3] concentrated on the shortcomings of star schemas in the context of slowly changing dimensions. The conclusion is that stateoriented warehouses allow easier analytical processing and even better query performance than observed in regular events warehouses. ....

....symbolic instants ( e.g. by pre defined date constants. Indexes are even more important in temporal relations due to their size. Furthermore, the focus of temporal analytical processing on coalescing (merging value equivalent tuples with intervals that overlap or meet) and temporal joins [10] requires appropriate indexes [13] In temporal queries, conjunctions of inequality predicates (which are harder to optimize) appear more frequently. Consequently, improved cost models for the optimization of temporal operators have to be applied. 3.3. Data Staging While some source systems ....

Jensen, C.S., Snodgrass, R.T.: Temporal Data Management. In IEEE Transactions on Knowledge and Data Engineering, Vol. 11(1): pp. 36-44, 1999.

....space. We assume the First Temporal Normal Form [SS88] which implies that no two records exist in a given temporal relation that have equal keys and intersecting intervals. A record with time interval i is called alive for all time instants in i. Moreover, we assume the transaction time model ([JS99]) which implies that record updates arrive in increasing time order. When a data record is inserted in a relation at time t, the end time of its interval is yet unknown and is thus initiated to now (a variable representing the ever increasing current time) Record deletions are logical, i.e. ....

C. Jensen and R. Snodgrass, "Temporal Data Management", TKDE 11(1), pp. 36-44, 1999. (also appear as TimeCenter Tech Report 17)

....space. We assume the First Temporal Normal Form [SS88] which implies that no two records exist in a given temporal relation that have equal keys and intersecting intervals. A record with time interval i is called alive for all time instants in i. Moreover, we assume the transaction time model ([JS99]) which implies that record updates arrive in increasing time order. When a data record is inserted in a relation at time t, the end time of its interval is yet unknown and is thus initiated to now (a variable representing the ever increasing current time) Record deletions are logical, i.e. ....

C. Jensen and R. Snodgrass, "Temporal Data Management", TKDE 11(1), pp. 36-44, 1999. (also appear as TimeCenter Tech Report 17)

....be modi ed. Partial persistence ts nicely with the degenerate evolution case since in that case an update corresponds to either an object addition or a deletion. Methods to make a disk based structure (in particular a B tree) partially persistent have appeared in the area of temporal databases [20, 5, 27, 49, 24, 36]. 24] presents the Bitemporal R Tree which is a partiallypersistent R Tree used to index bitemporal objects. This partially persistent R Tree can be easily extended to index the degenerate case of animated objects. The general evolution case where objects change continuously is di erent. One ....

C.S. Jensen and R.T. Snodgrass. Temporal Data Management. In IEEE TKDE, 11(1): 36-44, 1999.

....fits nicely with the degenerate case of the problem we address. This is because in the degenerate case an update simply corresponds to object additions deletions. Methods to make a disk based structure (in particular a B tree) partially persistent have appeared in the area of temporal databases [20, 5, 26, 46, 24]. 24] presents the Bitemporal R Tree which is a partially persistent R Tree used to index bitemporal objects. This partiallypersistent R Tree can be easily extended to index the degenerate case of animated objects. However the general case where objects change continuously is different. One ....

C.S. Jensen and R.T. Snodgrass. Temporal Data Management. In IEEE TKDE, 11(1): 36-44, 1999.

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C. S. Jensen and R. T. Snodgrass. Temporal Data Management. IEEE TKDE, 11(1): 36--45 (1999).

No context found.

C. Jensen and R.T. Snodgrass, "Temporal Data Management," IEEE Transactions on Knowledge and Data Engineering 11(1), 36--44, 1999.

....be modified. Partial persistence fits nicely with the degenerate evolution case since in that case an update corresponds to either an object addition or a deletion. Methods to make a disk based structure (in particular a B tree) partially persistent have appeared in the area of temporal databases [20, 5, 27, 49, 24, 36]. 24] presents the Bitemporal R Tree which is a partially persistent R Tree used to index bitemporal objects. This partiallypersistent R Tree can be easily extended to index the degenerate case of animated objects. The general evolution case where objects change continuously is different. One ....

C.S. Jensen and R.T. Snodgrass. Temporal Data Management. In IEEE TKDE, 11(1): 36-44, 1999.

....and recall. Performance studies indicate that the technique is competitive with the best existing index; and unlike this existing index, the new technique does not require extension of the DBMS kernel. 1. Introduction Two temporal aspects of data are fundamental valid time and transaction time [10]. The valid time of a database tuple is the time when the tuple is true in the modeled reality, the mini world. A tuple s transaction time is the time during which the tuple is current in the database. These temporal aspects of data are essential in a wide range of existing, real world ....

....more detail the nature of bitemporal data, then characterizes the different kinds of two dimensional bitemporal data regions. As mentioned in the previous section, valid time captures when a tuple is true in the modeled reality, and transaction time captures when a tuple is current in the database [10]. These two temporal aspects are orthogonal in that each could be recorded independently, and each has specific properties associated with it. The valid time of a tuple can be in the past or in the future and can be changed freely. In contrast, the transaction time of a tuple cannot extend beyond ....

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C. S. Jensen and R. Snodgrass. Temporal Data Management. IEEE TKDE, 11(1):36--44, 1999.

....1. Introduction Most real world database applications manage timereferenced data. For example, this aspect applies to financial, medical, and travel applications; and being timevariant is one of Inmon s defining properties of a data warehouse [11] Recent advances in temporal query languages [8, 13] show that such applications may benefit substantially from a DBMS with built in temporal support. The potential benefits are several: application code is simplified and more easily maintainable, thereby increasing programmer productivity [21] and more data processing can be left to the DBMS, ....

C. S. Jensen and R. T. Snodgrass. Temporal Data Management. IEEE TKDE, 11(1): 36--45 (1999).

No context found.

C. S. Jensen and R. T Snodgrass. Temporal Data Management. In IEEE Transactions on Knowledge and Data Engineering, Vol.11, No.1, pp.36-44, 1999

No context found.

C.S. Jensen and R.T. Snodgrass. Temporal Data Management. In IEEE TKDE, 11(1): 36-44, 1999.

No context found.

C. Jensen and R. Snodgrass, \Temporal Data Management", TKDE 11(1), 1999.

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