Automated travel planning

This paper summarizes the current state of art in the domain of automated travel planning. Requirements for planning systems are identified taking into account both functionality and personalization aspects of such systems. A new algorithm that allows planning routes between any two locations and that utilizes combination of various means of transportation is discussed

Ontology-based stereotyping in a travel support system

The aim of this paper is to address the problem of user profile initialization in a travel support system. In the system under consideration, ontologically demarcated data is stored in a central repository, while user profiles are functionalized as instances of travel object ontologies. Creation of an initial user profile is achieved through stereotyping. An example of utilization of this technique, in the case of restaurant stereotypes, is presented

The implementation and analysis of parallel algorithm for finding perfect matching in the bipartite graphs

There exists a large number of theoretical results concerning parallel algorithms for the graph problems. One of them is an algorithm for the perfect matching problem, which is also the central part of the algorithm for finding a maximum flow in a net. We have attempted at implementing it on a parallel computer with 12 processors (instead of the theoretical 0(n^3.5m) processors). When pursuing this goal we have run into a number of practical problems. The aim of this paper is to discuss them as well as the experimental results of our implementation

Applying Machine Learning to Study Infrastructure Anomalies in a Mid-size Data Center -- Preliminary Considerations

Today, data centers deal with fast growing data volumes. To deliver services, they deploy growing amount of heterogeneous hardware. As a result, it becomes practically impossible to apply human-based data center management. For instance, in a real-world data center, with 500+ computers, delivering data, computational, and network services, it becomes impossible to visualize, and understand, causal relationships among variables describing performance of monitored resources. However, it is possible to collect data describing behavior of individual nodes. Hence, such data may be used to analyze/model system performance. In particular, it may be applied to recognize and predict anomalies in system behavior. Furthermore, collected data should allow finding the cause(s) of anomalies. Therefore, “data-driven approaches” have been applied to the real-world data, to find, so called, Root Cause of anomalies

Parallel implementation of the k-connectivity test algorithm

There exists a large number of theoretical results concerning fast parallel algorithms for graph problems, however, scarcely one finds reports of their practical implementation. In an attempt at partial filling this gap we discuss implementation of an algorithm performing the pretest for k-connectivity. This test is based, first, on the Scan-First Search algorithm introduced in [1]. Utilizing this procedure we decrease the size of the input graph by removing selected edges so that the resulting graph (certificate of k-connectivity) has only 0(kn) left. During this part of computations we can answer the question about k-connectivity negatively if a certificate cannot be generated. Afterwards, we can apply the test described in [2] to establish ^-connectivity in the remaining cases