We consider the problem of processing K-Nearest Neighbor (KNN) queries over large data
sets where the index is jointly maintained by a set of machines in a computing cluster. The …

The NP-hard Max-k-cover problem requires selecting k sets from a collection so as to
maximize the size of the union. This classic problem occurs commonly in many settings in …

In Information Retrieval (IR), the efficient indexing of terabyte-scale and larger corpora is still
a difficult problem. MapReduce has been proposed as a framework for distributing data …

Data indexing is common in data mining when working with high-dimensional, large-scale
data sets. Hadoop, a cloud computing project using the MapReduce framework in Java, has …

In TREC 2009, we extend our Voting Model for the faceted blog distillation, top stories
identification, and related entity finding tasks. Moreover, we experiment with our novel …

The FPgrowth is a famous frequent pattern's algorithm in data mining when working with
high-dimensional, large-scale data sets. It is also known as great complexity on memory for …

Data abundance poses the need for powerful and easy‐to‐use tools that support processing
large amounts of data. MapReduce has been increasingly adopted for over a decade by …

The goal of data mining is to discover hidden useful information in large databases. Mining
frequent patterns from transaction databases is an important problem in data mining. As the …

Information Retrieval (IR) systems require input corpora to be indexed. The advent of
terabyte-scale Web corpora has reinvigorated the need for efficient indexing. In this work, we …

Formulating and processing phrases and other term dependencies to improve query
effectiveness is an important problem in information retrieval. However, accessing word …