January/February 2018 issue of acmqueue

The January/February issue of acmqueue is out now


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Originally published in Queue vol. 7, no. 11
see this item in the ACM Digital Library



Graham Cormode - Data Sketching
The approximate approach is often faster and more efficient.

Heinrich Hartmann - Statistics for Engineers
Applying statistical techniques to operations data

Pat Helland - Immutability Changes Everything
We need it, we can afford it, and the time is now.

R. V. Guha, Dan Brickley, Steve MacBeth - Schema.org: Evolution of Structured Data on the Web
Big data makes common schemas even more necessary.


(newest first)

Christopher John Finegan | Wed, 23 Dec 2009 15:12:26 UTC

Julian Hyde dismissively chose to pontificate and refer to relational model as not theoretically pure, while I would argue he is completely wrong, and relational model is perhaps TOO much so. C.J. Date's books on bitemporal data and on the third manifesto which do indeed extend the relational model basis on predicate calculus to "nested tables" on a theoretically sound basis.

What I Found extremely disappointing, is a completely biased representation with no regard to "Queue" tables which have been in relational database implementations for decades, and additionally, lack of comparison of tradeoffs to quantity and quality of stream optimizer and massively parallel processing and globally distributed computing capabilities and decisioning and analytics relative to interlacing data across multiple heterogeneous "streams" integrated with historical data facts that must be leveraged and exploited for complete and proper decisioning on streams, that might be considered not just "OLAP" information from historical databases and datastrores. Moving function to the data, not data to the function is the mantra, and distributed data streams, replicated across massively parallell processing engines in a grid or cloud need to be managed and controlled by a hypervisor/operating system for prudent workload management and load balancing, so a balance of where and when the "data services" layer requires a blend across data streaming in and stored as input to processing on data, managed by a network of RDBMS loosely-coupled engines that are not either ALL stream or ALL DBMS, but a managed balancing act of the two. This is far too one-sided simplistic representation as if streams are different than the database, they are one and the same.

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