InterViso: Dealing with the complexity of federated database access

Abstract.A data warehouse (DW) is supplied with data that come from external data sources (EDSs) that are production systems. EDSs, which are usually autonomous, often change not only their contents but also their structures. The evolution of external data sources has to be reflected in a DW that uses the sources. Traditional DW systems offer a limited support for handling dynamics in their structures and contents. A promising approach to this problem is based on a multiversion data warehouse (MVDW). In such a DW, every DW version includes a schema version and data consistent with its schema version. A DW version may represent a real state at certain period of time, after the evolution of EDSs or changed user requirements or the evolution of the real world. A DW version may also represent a given business scenario that is created for simulation purposes. In order to appropriately synchronize a MVDW content and structure with EDSs as well as to analyze multiversion data, a MVDW has to manage metadata. Metadata describing a MVDW are much more complex than in traditional DWs. In our approach and prototype MVDW system, a metaschema provides data structures that support: (1) monitoring EDSs with respect to content and structural changes, (2) automatic generation of processes monitoring EDSs, (3) applying the discovered EDS changes to a selected, DW version, (4) describing the structure of every DW version, (5) querying multiple DW versions of interest at the same time, (6) presenting and comparing multiversion query results.

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“…[8,9,13,30,43]. A lot of research have been carried out in order to handle schema changes and propagate them into a global schema, e.g.…”

Section: Related Workmentioning

confidence: 99%

Metadata Management in a Multiversion Data Warehouse

Wrembel

Bębel

2005

Lecture Notes in Computer Science

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“…Bushy plans are generally less vulnerable to delays since different branches of tree can be found that are not directly affected by the delayed relations; such subtrees can be executed independently. The research prototype Mermaid [CBTY89] and its commercial successor InterViso [THMB95] are heterogeneous distributed databases that perform dynamic query optimization. Mermaid constructs its query plan entirely at run-time, thus each step in query optimization is based on dynamic information such as the intermediate relation cardinalities and system performance.…”

Section: Related Workmentioning

confidence: 99%

Cost-based query scrambling for initial delays

1998

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Remote data access from disparate sources across a wide-area network such as the Internet is problematic due to the unpredictable nature of the communications medium and the lack of knowledge about the load and potential delays at remote sites. Traditional, static, query processing approaches break down in this environment because they are unable to adapt in response to unexpected delays. Query scrambling has been proposed to address this problem. Scrambling modifies query execution plans on-the-fly when delays are encountered during runtime. In its original formulation, scrambling was based on simple heuristics, which although providing good performance in many cases, were also shown to be susceptible to problems resulting from bad scrambling decisions. In this paper we address these shortcomings by investigating ways to exploit query optimization technology to aid in making intelligent scrambling choices. We propose three different approaches to using query optimization for scrambling. These approaches vary, for example, in whether they optimize for total work or response-time, and whether they construct partial or complete alternative plans. Using a two-phase randomized query optimizer, a distributed query processing simulator, and a workload derived from queries of the TPC-D benchmark, we evaluate these different approaches and compare their ability to cope with initial delays in accessingremote sources. The results show that cost-based scrambling can effectively hide initial delays, but that in the absence of good predictions of expected delay durations, there are fundamental tradeoffs between risk aversion and effectiveness.

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“…The queries accompanying steps 9 to 15 show h o w the constructs of S 1 can be restored from those of S g , and the queries accompanying steps 16 to 23 show h o w to the constructs of S 2 can be restored from S g .…”

Section: Integrating S 1 and S 2 Into S Gmentioning

confidence: 99%

Data integration by bi-directional schema transformation rules

McBrien

Poulovassilis²

Proceedings 19th International Conference on Data Engineering (Cat. No.03CH37405)

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This paper presents a new approach t o data integration, which combines the previous approaches of local as view (LAV) and global as view (GAV) into a single method we t e r m bothas view (BAV). Our method is based on the use of reversible transformation sequences, and combines the respective a d v antages of GAV and LAV. In particular, in common with GAV, querying processing on a global schema is simply a matter of view rewriting, while in common with LAV, our approach is scalable as local schemas evolve, are added or deleted. We also also show how LAV and GAV view de nitions can be fully derived from BAV schema transformation sequences, and how BAV transformation sequences may be partially derived from LAV or GAV view de nitions.

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InterViso: Dealing with the complexity of federated database access

Cited by 25 publications

References 19 publications

Metadata Management in a Multiversion Data Warehouse

Metadata Management in a Multiversion Data Warehouse

Cost-based query scrambling for initial delays

Data integration by bi-directional schema transformation rules

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