Advances in information technology and its widespread growth in several areas of business,
engineering, medical, and scientific studies are resulting in information/data explosion …

Apache Spark: a unified engine for big data processing Page 1 56 COMMUNICATIONS OF THE
ACM | NOVEMBER 2016 | VOL. 59 | NO. 11 contributed articles DOI:10.1145/2934664 This …

MapReduce is a programming system for distributed processing of large-scale data in an
efficient and fault tolerant manner on a private, public, or hybrid cloud. MapReduce is …

We present a simple polylogarithmic-time deterministic distributed algorithm for network
decomposition. This improves on a celebrated 2 O (√ log n)-time algorithm of Panconesi …

Over half a century old and showing no signs of aging, k-means remains one of the most
popular data processing algorithms. As is well-known, a proper initialization of k-means is …

We study the problem of computing conjunctive queries over large databases on parallel
architectures without shared storage. Using the structure of such a query q and the skew in …

The algorithms community has been modeling the underlying key features and constraints of
massively parallel frameworks and using these models to discover new algorithmic …

Our world is being revolutionized by data-driven methods: access to large amounts of data
has generated new insights and opened exciting new opportunities in commerce, science …

The clustering coefficient of a node in a social network is a fundamental measure that
quantifies how tightly-knit the community is around the node. Its computation can be reduced …

Numerous graph mining applications rely on detecting subgraphs which are large near-
cliques. Since formulations that are geared towards finding large near-cliques are hard and …