Abstract: The increasing importance of data stream arising in a
wide range of advanced applications has led to the extensive study of
mining frequent patterns. Mining data streams poses many new
challenges amongst which are the one-scan nature, the unbounded
memory requirement and the high arrival rate of data streams. In this
paper, we propose a new approach for mining itemsets on data
stream. Our approach SFIDS has been developed based on FIDS
algorithm. The main attempts were to keep some advantages of the
previous approach and resolve some of its drawbacks, and
consequently to improve run time and memory consumption. Our
approach has the following advantages: using a data structure similar
to lattice for keeping frequent itemsets, separating regions from each
other with deleting common nodes that results in a decrease in search
space, memory consumption and run time; and Finally, considering
CPU constraint, with increasing arrival rate of data that result in
overloading system, SFIDS automatically detect this situation and
discard some of unprocessing data. We guarantee that error of results
is bounded to user pre-specified threshold, based on a probability
technique. Final results show that SFIDS algorithm could attain
about 50% run time improvement than FIDS approach.
Abstract: Selection of the best possible set of suppliers has a
significant impact on the overall profitability and success of any
business. For this reason, it is usually necessary to optimize all
business processes and to make use of cost-effective alternatives for
additional savings. This paper proposes a new efficient context-aware
supplier selection model that takes into account possible changes of
the environment while significantly reducing selection costs. The
proposed model is based on data clustering techniques while
inspiring certain principles of online algorithms for an optimally
selection of suppliers. Unlike common selection models which re-run
the selection algorithm from the scratch-line for any decision-making
sub-period on the whole environment, our model considers the
changes only and superimposes it to the previously defined best set
of suppliers to obtain a new best set of suppliers. Therefore, any recomputation
of unchanged elements of the environment is avoided
and selection costs are consequently reduced significantly. A
numerical evaluation confirms applicability of this model and proves
that it is a more optimal solution compared with common static
selection models in this field.