Abstract: With the advance of multimedia and diagnostic
images technologies, the number of radiographic images is increasing
constantly. The medical field demands sophisticated systems for
search and retrieval of the produced multimedia document. This
paper presents an ongoing research that focuses on the semantic
content of radiographic image documents to facilitate semantic-based
radiographic image indexing and a retrieval system. The proposed
model would divide a radiographic image document, based on its
semantic content, and would be converted into a logical structure or
a semantic structure. The logical structure represents the overall
organization of information. The semantic structure, which is bound
to logical structure, is composed of semantic objects with
interrelationships in the various spaces in the radiographic image.
Abstract: This paper proposes an auto-classification algorithm
of Web pages using Data mining techniques. We consider the
problem of discovering association rules between terms in a set of
Web pages belonging to a category in a search engine database, and
present an auto-classification algorithm for solving this problem that
are fundamentally based on Apriori algorithm. The proposed
technique has two phases. The first phase is a training phase where
human experts determines the categories of different Web pages, and
the supervised Data mining algorithm will combine these categories
with appropriate weighted index terms according to the highest
supported rules among the most frequent words. The second phase is
the categorization phase where a web crawler will crawl through the
World Wide Web to build a database categorized according to the
result of the data mining approach. This database contains URLs and
their categories.
Abstract: In this paper, a novel multi join algorithm to join
multiple relations will be introduced. The novel algorithm is based
on a hashed-based join algorithm of two relations to produce a double index. This is done by scanning the two relations once. But
instead of moving the records into buckets, a double index will be built. This will eliminate the collision that can happen from a complete hash algorithm. The double index will be divided into join
buckets of similar categories from the two relations. The algorithm then joins buckets with similar keys to produce joined buckets. This
will lead at the end to a complete join index of the two relations. without actually joining the actual relations. The time complexity
required to build the join index of two categories is Om log m where m is the size of each category. Totaling time complexity to O n log m
for all buckets. The join index will be used to materialize the joined relation if required. Otherwise, it will be used along with other join
indices of other relations to build a lattice to be used in multi-join operations with minimal I/O requirements. The lattice of the join indices can be fitted into the main memory to reduce time complexity of the multi join algorithm.