BERTINO E., OOI B. C., TAN R. S.-D. K.-L., ZOBEL J., SHIDLOVSKY B., CATA- NIA B., Indexing Techniques for Advanced Database Systems, Kluwer Academic, Boston, Massachussets, 1997, 250 p.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....until a data pointer is reached. The performance of the proposed D tree has been thoroughly evaluated using both synthetic and real datasets [27] The result showed that overall the D tree performs much better than the typical object decomposition and object approximation based index structures [4, 5, 6]. 5. CONCLUSION AND FUTURE WORK In recent years, mobile computing has gained much attention from all of the government, industrial and academic sectors. This paper gives an overview of the characteristics of mobile computing environments, the system model, and related data management issues. In ....

E. Bertino, B. C. Ooi, R. Sacks-Davis, K. L. Tan, J. Zobel, B. Shilovsky, and B. Catania. Indexing techniques for advanced database systems. Boston: Kluwer Academic, 1997.

....be pruned away without evaluation, further enhancing the efficiency of the scheme. Experimental results show that iMinMax( can outperform the more complex Pyramid technique by a wide margin. 1. INTRODUCTION Many multi dimensional indexing structures have been proposed in the literature (see [1] for a survey) In particular, it has been observed that the performance of hierarchical tree index structures such as R trees [2] and R trees [3] deteriorates rapidly with the increase in the dimensionality of data. This phenomenon is caused by two factors. First, let us consider the fan out of ....

....to additional page accesses and CPU cost, causing the index to degenerate into a semi sequential scan within an index. Second, as the number of dimensions increases, the area covered by the query increases tremendously. Consider a hyper cube with a selectivity of 0. 1 of the domain space ([0,1], 0,1] 0,1] This is a relatively small query in two to three dimensional databases. However, for a 40dimensional space, the query width along each dimension works out to be 0.841, which causes the query to cover a large area of the domain space. Consequently, many leaf nodes of a ....

[Article contains additional citation context not shown here]

E. Bertino, et. al. Indexing Techniques for Advanced Database Systems. Chapters 2, 3. 39-75, Kluwer Academic Publishers, Boston, 1997.

.... and genome databases (i.e. FASTA SWAP Pattern database at http: dot.imgen.bot.tmc.edu:9331) We pay particular attention to the translation of Boolean queries into a Web page language which supports the ranking model for query formulation and for the feedback relevance [1]. The ranking model is used by many Web services and fundamentally different from the Boolean query model. An example of a Web service based on the ranking query model is the Yahoo search engine (http: www.yahoo.com) The goal of the Yahoo service is a wider search and better ranking of the ....

E. Bertino, B. C. Ooi, R. Sacks-Davis et al. Indexing Techniques for Advanced Database Systems, Kluwer Academic Publishing, 1997.

....relies on the underlying indexes and their ecient support for ltering out dissimilar objects. As such, many indexing techniques have been proposed. The R tree [11] and its variants [1, 17] are known to yield good performances compared to a plethora of competing multi attribute indexing structures [5, 15]. However, it has been observed that the performances of R tree based index structures deteriorate rapidly when the dimensionality of data is high [4, 5] This is because overlap in the directory increases rapidly with growing dimensionality of data, requiring multiple subtrees to be searched for ....

.... [11] and its variants [1, 17] are known to yield good performances compared to a plethora of competing multi attribute indexing structures [5, 15] However, it has been observed that the performances of R tree based index structures deteriorate rapidly when the dimensionality of data is high [4, 5]. This is because overlap in the directory increases rapidly with growing dimensionality of data, requiring multiple subtrees to be searched for each query. This is partly due to the low fanout as a result of increasing dimensionality; the fan out of an R tree is somewhat inversely proportional to ....

E. Bertino and et. al. Indexing Techniques for Advanced Database Systems. Kluwer Academic, 1997.

....be pruned away without evaluation, further enhancing the efficiency of the scheme. Experimental results show that iMinMax( can outperform the more complex Pyramid technique by a wide margin. 1. INTRODUCTION Many multi dimensional indexing structures have been proposed in the literature (see [1] for a survey) In particular, it has been observed that the performance of hierarchical tree index structures such as R trees [2] and R trees [3] deteriorates rapidly with the increase in the dimensionality of data. This phenomenon is caused by two factors. First, let us consider the fan out of ....

....to additional page accesses and CPU cost, causing the index to degenerate into a semi sequential scan within an index. Second, as the number of dimensions increases, the area covered by the query increases tremendiously. Consider a hyper cube with a selectivity of 0. 1 of the domain space ([0,1], 0,1] 0,1] This is a relative small query in two to three dimensional databases. However, for a 40dimesional space, the query width along each dimension works out to be 0.841, which causes the query to cover a large area of the domain space. Consequently, many leaf nodes of a ....

[Article contains additional citation context not shown here]

E. Bertino, et. al. Indexing Techniques for Advanced Database Systems. Chapters 2, 3. 39-75, Kluwer Academic Publishers, Boston, 1997.

....points. Section 6 presents our performance study, and the results. Finally, we conclude in 7 with directions for future work. 2 Related Work Many indexing techniques have been proposed for nearest neighbor and approximate search in highdimensional databases. Existing multi dimensional indexes [4] such as R trees [11] have been shown to be inecient even for supporting range window queries in high dimensional databases; they, however, form the basis for indexes designed for high dimensional databases [12, 17] To reduce the e ect of high dimensionalities, use of bigger nodes [3] ....

E. Bertino and et. al. Indexing Techniques for Advanced Database Systems. Kluwer Academic, 1997.

....is processed for extracting region features. 3 Indexing technique To provide easy and fast access, the generated meta data have to be stored in suitable index structures. Logical structures are used to store normalized area and spatial location of the regions objects in the image. Bertino et al. [4] have suggested general image indexing taxonomy for image databases based on various image features. 3.1 Multi level multidimensional index A multi level multidimensional index structure based on partitioning and R tree [4] is used to store the images. The structure employs pruning to speed up ....

....and spatial location of the regions objects in the image. Bertino et al. 4] have suggested general image indexing taxonomy for image databases based on various image features. 3. 1 Multi level multidimensional index A multi level multidimensional index structure based on partitioning and R tree [4] is used to store the images. The structure employs pruning to speed up retrieval. Figure 5 shows the complete two level structure. 1 2 10 n (0,1) 0,2) 0,1,2) 0,2,1) Image Index Image Cluster Features . 1 Region Number of Regions 2 ....

E.Bertino et al. Indexing Techniques for Advanced Database Systems. Kluwer Academic Publishers, 1997.

....elements will hold C s first range, the next four elements will hold C s second range, etc. Here, ranges are stored in chronological order. Note that temporal constraints can be indexed by using an auxiliary data structure, e.g. a segment tree [40] or constraint indexing methods such as those in [5, 6, 22]. 7.1 Experiments We conducted two sets of experiments. The first set of experiments was intended to demonstrate the relative efficiency of TP algebra operations when compared to TATA algebra operations. In addition, this set of experiments was designed to study how different distribution ....

E. Bertino, B.C. Ooi, R. Sacks-Davis, K.L.Tan, J.Zobel, B.Shidlovsky, B.Catania. (1997) Indexing Techniques for Advanced Database Systems, Kluwer Academic Publishers, 1997.

....Figure 2: Possible Combinations of Time Attributes We have set the context for using spatial indices for indexing bitemporal data. There already exist some indices for bitemporal data that are based on this approach; we discuss them next. 3 Overview of the Existing Bitemporal Indices References [ST97, BER97] provide comprehensive surveys of indices for different types of temporal data. This section focuses solely on the indexing of bitemporal data. All existing indices for now relative bitemporal data are based on the idea that bitemporal data can be viewed as a special case of spatial data (recall ....

E. Bertino et al. Indexing Techniques for Advanced Database Systems. Kluwer Academic Publishers (1997).

....awating certain stocks to be priced in a certain range) The value of X is updated frequently (as are stock prices) but the number of reactors interested in a particular update to X (in a particular stock price change) is relatively small. The registry could employ a range indexing algorithm [2] in order to identify the appropriate subset of reactors. In summary, by adopting the implementation philosophy that there are many registered reactors, and any one store update is unrelated to the vast majority of them, the main job of the registry is in translating the blocking conditions of a ....

....declarative concurrency. The idea is that the constraints specify how occurrences of certain events may run relative to one another, and this clearly has general application to parallel programming. Consider event classes e; f; Delta Delta Delta, and event occurrences (or simply events) e[1] e[2]; Delta Delta Delta ; f[1] f[2] Delta Delta Delta. The item of real interest is the event class, and for this example, it suffices to think of a class as a certain program point in an agent. The constraints in our server are of type integer, and event classes are represented as sequences ....

[Article contains additional citation context not shown here]

Bertino, E., Ooi, B.C., Sacks-Davis, R., Tan, K.-L., Shidlovsky, B., and B. Catania, "Indexing Techniques for Advanced Database Systems", Kluwer Academic Publishers, 1997.

....employed for advanced applications such as object oriented databases, geographic and spatial databases, temporal databases, data warehousing, high dimensional databases, and even XML databases. Many specialized indexing structures were and continue to be proposed to deal with these applications [7]. Among these, there are always B tree based schemes being pursued. Two reasons account for this. First, as B tree is widely deployed in commercial systems, there is a need to be able to reuse it to allow these systems to cope with different applications demands. Second, solutions based on ....

E. Bertino, B.C. Ooi, R. Sacks-Davis, K.L. Tan, J. Zobel, B. Shilovsky, and B. Catania. Indexing Techniques for Advanced Database Systems. Kluwer Academic Publishers, August 1997.

....likelihood of being perceived as relevant to the query [11, 14] An answer to such a ranked query is always a list of documents. Typically the list is of fixed length, that is, is independent of collection size. In such information retrieval systems, query evaluation proceeds as follows [2]. First each of the query terms is found in a lexicon, requiring approximately one disk access per term. Even if the vocabulary is organised as a B tree, the high branching factor and system disk caching ensure that all but the leaves are permanently held in memory. For each term, it is then ....

E. Bertino, B. Ooi, R. Sacks-Davis, K.-L. Tan, J. Zobel, B. Shidlovsky, and B. Catania. Indexing Techniques for Advanced Database Systems. Kluwer Academic Press, Boston, Massachusetts, 1997.

....including the frequency of query words in the documents and the relative rareness of the query words. Query evaluation in an efficient IR system is supported by an inverted index consisting of a vocabulary and, for each term in the vocabulary, a list of information about where the term occurs [1] [11] The simplest inverted index structure for evaluating ranked queries has, for each each word in the vocabulary, a list of documents that contain that word and a count of occurrences of that word in each document. The inverted list for the term Richmond might have the structure: 1:8;2: 11; ....

E. Bertino, B. Ooi, R. Sacks-Davis, K.-L. Tan, J. Zobel, B. Shidlovsky, and B. Catania. Indexing Techniques for Advanced Database Systems. Kluwer Academic Publishers, 1997.

....7. 2 Related Work In this section, we discuss related work on high dimensional indexing, clustering and dimensionality reduction. Multidimensional indexing has been extensively researched in the database literature. There are numerous proposals for indexing techniques in multiple dimensions[5]. A very good survey can be found in [11] Several 2 data structures such as R Trees [12] hB trees[17] TV Trees[16] SS trees[23] are designed for supporting fast searching in large multi dimensional databases. These trees have been shown to be efficient for small dimensions, and to be ....

E. Bertino, B. C. Ooi, R. Sacks-Davis, K. Tan, J. Zobel, B. Shidlovsky, and B. Cantania. Indexing techniques for advanced database systems. Kluwer Academics, 1997.

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BERTINO E., OOI B. C., TAN R. S.-D. K.-L., ZOBEL J., SHIDLOVSKY B., CATA- NIA B., Indexing Techniques for Advanced Database Systems, Kluwer Academic, Boston, Massachussets, 1997, 250 p.

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E. Bertino, B.C. Ooi, R. Sacks-Davis, K.L. Tan, J. Zobel, B. Shilovsky, and B. Catania, Indexing Techniques for Advanced Database Systems. Boston: Kluwer Academic, 1997.

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E. Bertino et al. Indexing Techniques for Advanced Database Systems. Kluwer Academic Publishers (1997).

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E. Bertino, B. C. OOI, R. Sacks-Davis, K.-L. Tan, J. Zobel, B. Shidlovsky, and B. Catania. Indexing Techniques for Advanced Database Systems. Kluwer, 1997.

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E. Bertino, B. Catania, B. C. Ooi, R. Sacks-Davis, K. L. Tan, J. Zobel, B. Shidlovsky, and B. Catania, Indexing Techniques for Advanced Database Systems, Kluwer Academic Publishers, Boston, 1997.

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E. Bertino, B. C. Ooi, R. Sacks-Davis, K. L. Tan, J. Zobel, B. Shilovsky, and B. Catania. Indexing Techniques for Advanced Database Systems. Boston: Kluwer Academic, 1997.

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E. Bertino, B. C. Ooi, R. Sacks-Davis, K-L Tan, J. Zobel, B. Shidlovsky, and B. Catania. Indexing Techniques for Advanced Database Systems. Kluwer Academic Publishers, 1997.

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E. Bertino, B. Catania, B. C. Ooi, R. Sacks-Davis, K. L. Tan, J. Zobel, B. Shidlovsky, and B. Catania, Indexing Techniques for Advanced Database Systems, Kluwer Academic Publishers, Boston, 1997.

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E. Bertino, B. C. Ooi, R. Sacks-Davis, K. L. Tan, J. Zobel, B. Shilovsky, and B. Catania. Indexing techniques for advanced database systems. Boston: Kluwer Academic, 1997.

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Bertino, E, Ooi, B C, Sacks-Davis, R, Tan, K L, Zobel, J, Shilovsky, B and Catania, B. Indexing Techniques for advanced Database systems. Kluwer Academic Publisher, August 1997.

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E. Bertino, B. C. Ooi, R. Sacks-Davis, K. L. Tan, J. Zobel, B. Shilovsky, and B. Catania. Indexing techniques for advanced database systems. Boston: Kluwer Academic, 1997.

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