MAD Skills: New Analysis Practices for Big Data

By Jeffrey Cohen, Brian Dolan, Mark Dunlap, Joseph M Hellerstein, Caleb Welton. Proceedings of the VLDB Endowment, 2009

550 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/4ce65de80f2c1ea86c8788d2ba12dd114409d609

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Citations in the corpus

A comparison of approaches to large-scale data analysis (2009, background): “Categories and Subject Descriptors H.2.4 [Database Management]: Systems—Parallel databases General Terms Database Applications, Use Cases, Database Programming”
Hadoop++ (2010, background): “On the other side, Yahoo! proposed Pig Latin [21, 23] as a SQL-like query language for MapReduce; Hive proposed a query language for MapReduce called Hive QL [8].”
Big Data computing and clouds: Trends and future directions (2013, background): “This is the case of issues such as data quality [36] and data provenance [37].”
Spark SQL: Relational Data Processing in Spark (2015, methodology): “The desire to expose analytics functions is also shared with MADlib [12], though the approach there is different, as MADlib had to use the limited interface of Postgres UDFs, while Spark SQL’s UDFs can be full-fledged Spark programs.”
Big data and cloud computing: current state and future opportunities (2011, background): “We conclude this discussion on analytical systems by highlighting a new direction in data analysis referred to as “deep analytics” [9,16].”
Materialization optimizations for feature selection workloads (2014, background): “Since then, there has been a recent resurgence in arrays as ﬁrst-class citizens [14,15,31].”
Shark: SQL and rich analytics at scale (2012, background): “Other research projects [12, 14] have demonstrated that it is possible to express certain machine learning algorithms in SQL and avoid moving data out of the database.”
Shark: fast data analysis using coarse-grained distributed memory (2012, background): “MADlib also describes data-parallel statistics and machine learning algorithms in SQL [17], though declaring machine learning algorithms directly in SQL is not always convenient.”
Analytics over large-scale multidimensional data: the big data revolution! (2011, background): “It has been highlighted that Hadoop is a kind of MAD system [7] meaning that (i) it is capable of attracting all data sources (M standing for Magnetism), (ii) it is capable of adapting its engines to evolutions that may occur in big data sources (A standing for Agility), (iii) it is capable of suppo”
Big data analytics in supply chain management between 2010 and 2016: Insights to industries (2018, background): “For example, Cohen et al. (2009) proposed data-parallel statistical algorithms to do a sophisticated statistical analysis of big data.”
Big Data Analytics: A Literature Review Paper (2014, unknown): “Finally, since current data analyses use complex statistical methods, and analysts need to be able to study enormous datasets by drilling up and down, a big data repository also needs to be deep, and serve as a sophisticated algorithmic runtime engine [5].”