Ming-Chuan Wu, Alejandro P. Buchmann. Research Issues in Data Warehousing. BTW German Database Conference, 1997.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....4. Finally, Section 5 presents our conclusion and our current work on the design of relational DW. 2. An overview of Data Warehousing DW is a very wide research area. It has many different sub areas and it can be treated with different approaches. Some overviews of the research area are [Wid95][Wu97][Cha97] OLTP: On Line Transaction Processing 3 The global architecture of DW systems considered in most works is the one shown in Figure 2.1, although there is a variant that is proposed in [Inm96] a DW architecture with a Operational Data Store (ODS) This architecture is shown in Figure ....

Ming-Chuan Wu, Alejandro P. Buchmann. Research Issues in Data Warehousing. BTW German Database Conference, 1997.

....and show how to superimpose conventional multidimensional data warehouses with temporal master data to allow queries spanning multiple periods to return correct answers. 1 Introduction A data warehouse (DWH) is an integrated repository for data stemming from several heterogenous database systems [7, 5]. This source data may be structured, semi structured or unstructured. The most important usage of data warehouses is On Line Analytical Processing (OLAP) typically using a multi dimensional view of the data. OLAP tools then allow to aggregate and compare data along dimensions relevant to the ....

M. Wu and A. Buchmann. Research Issues in Data Warehousing. BTW'97, 1997. 6

....analyzing and presenting information derived from workflow logs. For further and deeper analysis, a data warehouse can also be used as base for data mining and knowledge discovery techniques. Data Warehouses are structured collections of data supporting controlling, decision making and revision [22, 10, 12]. Data Warehouses build the basis for analyzing data by means of OLAP tools which provide sophisticated features for aggregating, analyzing, and comparing data, and for discovering irregularities. Data Warehouses di#er from traditional databases in the following aspects: They are designed tuned ....

M. Wu and A. Buchmann. Research Issues in Data Warehousing. BTW'97, 1997.

....R. Pieringer, V. Markl, F. Ramsak, R. Bayer 11 2 with classify relationships. In [LW96] a MD model is discussed, based on relational elements. Many publications propose first to establish the conceptual model and then to do the actual implementation ([WB97], CT98] GMR98] HLV00] show how to systematically derive a conceptual warehouse schema from a generalized multidimensional normal form. FS99] introduce a conceptual data model, that allows complex descriptions of the structure of aggregated entities and multiply hierarchically organized ....

M.-C. Wu, A. P. Buchmann, Research Issues in data warehousing. Proc. Zth BTW 1997, 61-82

....grant #IRIS 97 29878, and the NSF grant #IIS 97 32897. Dr. Rundensteiner would like to thank our industrial sponsors, in particular, IBM for the IBM partnership award, and Verizon Laboratories Incorporated for partial support of Xin Zhang. 1 Introduction Background. Data warehousing [WB97, Wid95, GMLWZ98, GM96] is an important technology to integrate data from distributed information sources (ISs) in order to provide data to for example decision support or data mining applications [CD97] Once a DW is established, the problem of maintaining it consistent with the underlying ISs ....

M. Wu and A. P. Buchman. Research Issues in Data Warehousing. In Datenbanksysteme in Buro, Technik und Wissenschaft, pages 61--82, 1997.

....between the keys and the dimension tables and the corresponding foreign keys of the fact table. If the join indexes are represented in bitmap 24 matrices, a multiple join could be replaced by a sequence of bitwise operations, followed by a relatively small number of fetch and join operations [71]. 5.1.5 Data Index A DataIndex, like the projection index, exploits the positional indexing strategy [24] The DataIndex avoids duplication of data by storing only the index and not the column being indexed. The dataindex can be of two specific types: Basic DataIndex and Join DataIndex (for more ....

M-C. Wu and A. Buchmann. Research issues in data warehousing. in Datenbanksysteme in Buro, Technik und Wissenschaft(BTW'97), pages 61--82, March 1997.

....demonstrates the power of deductive rules for providing several useful data integration tools for data warehousing, such as data cleansing, integrity checking, calculation and summarization. It must be noted that such operations are closely related to resolving heterogeneity at schema integration [38]. 6.1 Data Cleansing It is important that the data in the warehouse be correct, since a Data Warehouse is used for decisionmaking [13] However, large volumes of data from multiple sources are involved, therefore there is a high probability of errors and anomalies in the data. Therefore, ....

M.-C. Wu and A. Buchmann, "Research Issues in Data Warehousing," in Proc. BTW 1997, pp. 61-82.

....specification that includes multiple sources. 1. Introduction In less than ten years, data warehousing has become a well established database research field, attracting attention from researchers in related areas. Numerous definitions of data warehousing can be found in the literature (e.g. [Cha97, Gup95, Lom95, Vav99, Wu97]) From a user perspective, a data warehouse (or DW) is a collection of cleaned, integrated, summarized data, which is available for on line analytical queries and decision making [Wu97] From a system perspective, a data warehouse is a database that collects and stores information from multiple ....

....areas. Numerous definitions of data warehousing can be found in the literature (e.g. Cha97, Gup95, Lom95, Vav99, Wu97] From a user perspective, a data warehouse (or DW) is a collection of cleaned, integrated, summarized data, which is available for on line analytical queries and decision making [Wu97]. From a system perspective, a data warehouse is a database that collects and stores information from multiple data sources [Gup95] As such, the contents of the warehouse can be described as a set of materialized views based on distributed, heterogeneous, and autonomous sources [Ham95] Reasons ....

M.C. Wu, A.P. Buchmann, "Research Issues in Data Warehousing", BTW'97, 1997

....This process comprises the phases requirements analysis, conceptual schema design, logical schema design,andphysical schema design. Concerning data warehouse design, there is a general agreement that at least a conceptual or logical modeling activity should precede the actual implementation [WB97, AGS97, CT98, GMR98]. Typically, the modeling activity is based on a multidimensional model (see [BSHD98, VS99, PJ99] for comparisons of various multidimensional models) whereas the implementation is carried out either within relational or multidimensional databases [CD97] However, most of these models were ....

....along x to one relationships in order to determine dimensions and their hierarchies. Concerning these two approaches, we critically remark the following. In [CT98] conceptual and logical design are mixed, and a logical multidimensional model is presented. We argue (in accordance with, e.g. [WB97]) that B. H usemann, J. Lechtenb orger, G. Vossen 6 2 t ur nov er balance creditlim it in terest custName orgName account orgGroup orgType pr oduc t ID balanceClass custID customerType orgID businessector accountID productType turnoverClass month effectiveDay quarter year custAge ....

M.-C. Wu, A. P. Buchmann, "Research issues in data warehousing," Proc. 7th BTW 1997, 61--82. B. H usemann, J. Lechtenb orger, G. Vossen 6-11

....MVSG graph is acyclic. Theorem has been proved. Finally by Theorems 3 and 2, we prove the ShadowWrapper algorithm to be correct. 9 Related Work Maintaining materialized views under source updates in a data warehouse environment is one of the important issues of data warehousing [GM95, Wid95, WB97, GMLWZ98] Initially, some research has studied incremental view maintenance assuming no concurrency. Such algorithms for maintaining a data warehouse under source data updates are called view maintenance algorithms [SI84, BLT86, GMS93, QW91, CGL 96, GL95, LMSS95] There has also been some ....

M. Wu and A. P. Buchman. Research Issues in Data Warehousing. In Datenbanksysteme in Buro, Technik und Wissenschaft, pages 61-82, 1997. 38

....Web search engines falls into this grey area, and intelligent similarity searching of multimedia documents or scientific data exhibits similar problems [4, 24, 34, 65] Furthermore, one may encounter inherent tradeoffs between different notions of service quality. In a data warehouse environment [11, 85], for example, one may consider trading the completeness, accuracy, or timeliness of query results for faster response and thus quicker, although less profound decision support. It seems that a thorough understanding of the notion of service quality and its ramifications and implications is sorely ....

....environment that achieves a substantially higher degree of overall process quality and system dependability. Such synergetic effects need to be studied much more systematically, however. Unfortunately, the combination of different notions of quality can also lead to tradeoffs. In a data warehouse [11, 85] or push technology information feed [25, 27] for example, the sheer volume of the data and the manifold information desires dictate a careful system design so as to support the most insightful decision support needs at affordable costs. One may often be willing to trade off some quality of ....

Wu, M.C., Buchmann, A.P.: Research Issues in Data Warehousing, German Conference on Database Systems in Office, Engineering, and Scientific Applications, Ulm, Germany, 1997.

....to apply any existing incremental maintenance algorithm from the literature. We demonstrate that this registry based coordination approach (RyCo) indeed achieves consistency across all DWs. Keywords: Distributed Data Warehousing, View Maintenance, Registry. 1 Introduction Data warehousing [14, 6, 7, 15, 11, 10] (DW) is a popular technology to integrate data from heterogeneous information sources (ISs) in order to provide data to, for example, decision support or data mining applications [2] Once a DW is established, the problem of maintaining it consistent with underlying ISs under updates remains a ....

M. Wu and A. P. Buchman. Research Issues in Data Warehousing. In Datenbanksysteme in Buro, Technik und Wissenschaft, pages 61--82, 1997.

....proposed solutions based on a cost model, and demonstrate that the partitioned registry approach achieves substantially better performance than the registry solution. Keywords: Distributed Data Warehousing, View Maintenance, Registry, Partition, Data Update. 1 1 Introduction Data warehousing [WB97, GMLWZ98, GM96, ZR99] DW) is a popular technology to integrate data from heterogeneous information sources (ISs) in order to provide data to, for example, decision support or data mining applications [CD97] Once a DW is established, the problem of maintaining it consistent with underlying ISs ....

M. Wu and A. P. Buchman. Research Issues in Data Warehousing. In Datenbanksysteme in Buro, Technik und Wissenschaft, pages 61--82, 1997.

....3: A data warehouse system at buildtime and runtime recent data values. However, this s not a problem with regard to the periodical or long term analysis tasks for which a data warehouse is mainly built. More detailed comparisons between operational systems and data warehouses may be found in [16, 24, 120]. 1.2 Building the Data Warehouse Figure 3 depicts a typical architecture of data warehouse systems with the interactions that happen between the components at buildtime and runtime. The warehouse is populated with the data extracted from the sources during two individual processes: the initial ....

M.C. Wu and P. Buchmann. Research issues in data warehousing. In Datenbanksysteme in Buro, Technik und Wissenschaft (BTW '97), pages 61 --82, Ulm, Germany, 1997.

....process star joins. Section 5 gives simulation results, whereas Section 6 presents performance measurements on real world data. Section 7 draws conclusions and gives an outlook on future work. 2. Related Work The new requirements and research issues set by OLAP applications are summarized in [Wid95, WB97]. Besides the questions of data management (e.g. data cleansing, data maintenance) there are two issues of great importance. First, the question of providing a good data warehouse architecture combining a conceptual, a logical, and a physical data model. An overview of the most popular models ....

M.C. Wu and A.P. Buchmann. Research Issues in Data Warehousing. BTW'97. 1997.

....any change. Hence, our method maintains all the advantages offered by existing algorithms in the literature in particular SWEEP and PSWEEP, while also achieving the additional desired features of being non intrusive, efficient, flexible and well behaved. 1 1 Introduction Data warehousing (DW) [WB97, Wid95, GMLWZ98, GM96] is a popular technology to integrate data from heterogeneous information sources (ISs) in order to provide data to for example decision support or data mining applications [CD97] Once a DW is established, the problem of maintaining it consistent and upto date of the underlying ISs remains a ....

M. Wu and A. P. Buchman. Research Issues in Data Warehousing. In Datenbanksysteme in Bro, Technik und Wissenschaft, pages 61--82, 1997.

....Concurrent Data Updates. This work was supported in part by the NSF NYI grant #IRI 94 57609. Dr. Rundensteiner would also like to thank IBM for the IBM Partnership Award as well as for IBM Fellowships for her students. 1 Introduction 1. 1 Background on View Maintenance Data warehousing [WB97, Wid95, GMLWZ98, GM96] is a newly developed technology to integrate data from heterogeneous information sources (ISs) in order to provide such data to for example decision support or data mining applications [CD97] As depicted in Figure 1, a data warehousing (DW) system [LZW 97, YKL97, HGMW ....

M. Wu and A. P. Buchman. Research Issues in Data Warehousing. In Datenbanksysteme in Bro, Technik und Wissenschaft, pages 61--82, 1997.

....Figure 6: SALESPOINT hierarchy and its encoding two different alliances. In the real world, the relationships between hierarchy elements are not necessarily 1 : N , they could also be m : N as is the case in the above example. One essential operation of OLAP is the manipulation along dimensions [17], e.g. roll ups drilldowns, data analysis along dimension hierarchies. All these operations are based on selections on dimensions, or on dimension elements, e.g. selecting sales data of all companies in alliance Z. Therefore, data of the same dimension hierarchies is very likely to be accessed ....

M.C. Wu, A. Buchmann, Research Issues in Data Warehousing, Datenbanksysteme in Buro, Technik und Wissenschaft, Editor: K.R.Dittrich and A. Geppert, Springer Verlag, 1997.

....and its encoding Note that some companies join two different alliances. In the real world, the relationships between hierarchy elements are not necessarily 1 : N , they could also be m : N as is the case in the above example. One essential operation of OLAP is the manipulation along dimensions [17], e.g. roll ups drill downs, data analysis along dimension hierarchies. All these operations are based on selections on dimensions, or on dimension elements, e.g. selecting sales data of all companies in alliance Z. Therefore, data of the same dimension hierarchies is very likely to be accessed ....

M.C. Wu, A. Buchmann, Research Issues in Data Warehousing, Datenbanksysteme in Buro, Technik und Wissenschaft, Editor: K.R.Dittrich and A. Geppert, Springer Verlag, 1997.

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M.C. Wu and A.P. Buchmann. Research Issues in Data Warehousing. BTW'97. 1997.

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