Instance selection for big data based on locally sensitive hashing and double-voting mechanism

Instance-Based Matching of Large Ontologies Using Locality-Sensitive Hashing

In this paper, we describe a mechanism for ontology alignment using instance based matching of types (or classes). Instance-based matching is known to be a useful technique for matching ontologies that have different names and different structures. A key problem in instance matching of types, however, is scaling the matching algorithm to (a) handle types with a large number of instances, and (b...

Instance selection of linear complexity for big data

Over recent decades, database sizes have grown considerably. Larger sizes present new challenges, because machine learning algorithms are not prepared to process such large volumes of information. Instance selection methods can alleviate this problem when the size of the data set is medium to large. However, even these methods face similar problems with very large-to-massive data sets. In this ...

A Fuzzy TOPSIS Approach for Big Data Analytics Platform Selection

Big data sizes are constantly increasing. Big data analytics is where advanced analytic techniques are applied on big data sets. Analytics based on large data samples reveals and leverages business change. The popularity of big data analytics platforms, which are often available as open-source, has not remained unnoticed by big companies. Google uses MapReduce for PageRank and inverted indexes....

Voting-based instance selection from large data sets with MapReduce and random weight networks

Instance selection is an important preprocessing step in machine learning. By choosing a subset of a data set, it achieves the same performance of a machine learning algorithm as if the whole data set is used, and it enables a machine learning algorithm to be feasible for and to work effectively with large data sets. Based on voting mechanism, this paper proposes a large data sets instance sele...

Probabilistic Hashing Techniques for Big Data