Abstract: Nowadays, in some areas, data growth has reached the point where a single relational database is not enough. This big data phenomenon has first time appeared in areas like meteorology, sensor data analytics, Internet search, biological research, genomics, finance, and many more. The effective use of distributed resources in clusters or data warehouses depends on the selection of appropriate data format and best algorithms. The subject of this research is a methodology and approaches for optimal use of the resources of a distributed system for processing Big Data from sensor measurements.
Key words: Big Data, Hadoop, HDFS, Sensor Fusion
Mariya Evtimova, Ivan Momtchev
Abstract: It is proposed a model of hybrid ontology that is case based, but is also suitable for big data as it is implemented rules. This ontology is divided into three parts- crisp part, fuzzy and probability part and big data part. Ontology use theory of the fuzzy sets with addition of probability logic for the realization of the fuzzy and probability part of the ontology. This hybrid ontology is suitable for vague and uncertain reasoning as it improve the quality of the returned results. Quality of the returned results is very important in the medical domain.
Key words: Big data, Case based reasoning, Fuzzy logic, Ontology, Probability logic