By Antonio Corral, Joaquin Cañadas, Michael Vassilakopoulos (auth.), Yannis Manolopoulos, Skevos Evripidou, Antonis C. Kakas (eds.)
This booklet constitutes the completely refereed post-proceedings of the eighth Panhellenic convention on Informatics, PCI 2001, held in Nicosia, Cyprus in November 2001.
The 31 revised complete papers awarded have been rigorously chosen and stronger in the course of months of reviewing from 104 convention papers. The papers disguise the parts of databases, information mining and clever platforms, e-learning, human desktop interplay, picture processing, networks and structures, software program and languages, and theoretical laptop science.
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Additional info for Advances in Informatics: 8th Panhellenic Conference on Informatics, PCI 2001 Nicosia, Cyprus, November 8–10, 2001 Revised Selected Papers
The architecture of the OLAP server consists of three major layers: the storage layer, the processing layer and the visualization layer. Each of these layers contain a major component of the OLAP server. Figure 1 presents the major components of the ERATOSTHENES OLAP server aligned along these three layers. Also, the intersection of the server components with the aforementioned models of an OLAP system is depicted. At the back end lies the storage engine (in the so-called storage layer ), which is responsible for the eﬃcient access to the stored data.
Chunking is not a new concept in the relevant literature. Several works exploit chunks; to our knowledge, the ﬁrst paper to introduce the notion of the chunk was . Very simply put, a chunk is a subcube within a cube with the same dimensionality as the encompassing cube. A chunk is created by deﬁning distinct ranges of members along each dimension of the cube. In other words, by applying chunking to the cube we essentially perform a kind of grouping of data. It has been observed [21, 5] that chunks provide excellent clustering of cells, which results in less I/O cost when reading data from a disk and also better caching, if a chunk is used as a caching unit.
Di Dj h- surrogate processing Fig. 5. The abstract processing plan for star-queries over hierarchically clustered OLAP cubes codes, therefore we can use the term range to denote the h-surrogate speciﬁcation arising from the evaluation of the restriction on a single dimension. Once the h-surrogate ranges are produced, then the corresponding fact table data must be retrieved. This could be simply called “the evaluation of the star-join”. However, due to the underlying multidimensional structure, which physically organizes the fact table data, this so-called “join” is transformed to one or more simple multidimensonal range queries.
Advances in Informatics: 8th Panhellenic Conference on Informatics, PCI 2001 Nicosia, Cyprus, November 8–10, 2001 Revised Selected Papers by Antonio Corral, Joaquin Cañadas, Michael Vassilakopoulos (auth.), Yannis Manolopoulos, Skevos Evripidou, Antonis C. Kakas (eds.)