Time Series Data Collection & Analysis Cube is a system for collecting timestamped events and deriving metrics. By collecting events rather than metrics, Cube lets you compute aggregate statistics post hoc. It also enables richer analysis, such as quantiles and histograms of arbitrary event sets. Cube is built on MongoDB and available under the Apache License on GitHub. Collecting Data An event in
SIGMOD - Proceedings of the 2011 International Conference on Management of Data, ACM, New York, NY We consider extending decision support facilities toward large sophisticated networks, upon which multidimensional attributes are associated with network entities, thereby forming the so-called multidimensional networks. Data warehouses and OLAP (Online Analytical Processing) technology have proven t
Computing aggregates over a cube of several dimensions is a common operation in data warehousing. The standard SQL syntax is "GROUP relation BY dim1, dim2, dim3 WITH CUBE" – which in addition to all dim1-2-3, produces aggregations for just dim1, just dim1 and dim2, etc. NULL is generally used to represent "all". A presentation by Arnab Nandi describes how one might implement efficient cubing in Ma
LinkedIn has many analytical insight products such as "Who's Viewed My Profile?" and "Who's Viewed This Job?". At their core, these are multidimensional queries. For example, "Who's Viewed My Profile?" takes someone's profile views and breaks them down by industry, geography, company, school, etc to show the richness of people who viewed their profiles: Online analytical processing (OLAP) has been
Guest post by Prasanth Jayachandran , who has been working on implementing CUBE support for Pig, as part of the large-scale distributed cubing effort. Update: As per Dmitriy’s tweet: …the naive implementation is in. The scalable count distinct impl is pending 0.11 branching, will go into 0.12. The next version of Apache Pig will support the CUBE operator ( patch available here ). The CUBE operator
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