Following up on KMeans Clustering Now Running on Elastic MapReduce, Stephen Green has generously documented the steps that was necessary to get an example of k-Means clustering up and running on Amazon’s Elastic MapReduce (EMR) on the Apache Lucene Mahout wiki.
I got an update on the Oracle Business Rules product recently. Oracle is an interesting company - they have the components of decision management but do not yet have them under a single umbrella. For instance, they have in-database data mining (blogged about here), the Real Time Decisions (RTD) engine, event processing rules and so on. Anyway, this update was on business rules.
In the book The Art of War for Executives, Donald G. Krause interprets the following: “Sun Tsu notes, superior commanders succeed in situations where ordinary people fail because they obtain more timely information and use it more quickly.” For metadata professionals, this observation is increasingly relevant as more and more of the business seeks integration and federation, alignment with business goals and strategies, and agility - the ability to respond both quickly and accurately to change. Industry analysts and IT professionals are less focused on solutions to problems where metadata management plays a role but rather look more to metadata management as an overall strategy for the benefits it provides to multiple aspects of the whole organization.
Actually the conceptual model of EPN (event processing network) can be thought as a kind of data flow (although I prefer the term event flow - as what is flowing is really events). The processing unit is EPA (Event Processing Agent). There are indeed two types of input to EPA, which can be called "set-at-a-time" and "event-at-a-time". Typically SQL based languages are more geared to "set-at-a-time", and other languages styles (like ECA rule) are working "event-at-a-time". From conceptual point of view, an EPA get events in channels, one input channels may be of a "stream" type, and in other, the event flow one-by-one. As there are some functions that are naturally set-oriented and other that are naturally event-at-a-time oriented, and application may not fall nicely into one of them, it makes sense to have kind of hybrid systems, and have EPN as the conceptual model on top of both of them...
Open source is ready for EDW. Because open source is designed to be modular, an enterprise can start with one piece - say ETL or reporting - and can add on as needed. For comparable power and features an open source solution in this arena can cost 10 to 20 times less than a proprietary product. Whether large or small, companies today are being asked to do more with less. With open source, you can have an EDW without compromise.
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The development of the Internet in recent years has made it possible and useful to access many different information systems anywhere in the world to obtain information. While there is much research on the integration of heterogeneous information systems, most commercial systems stop short of the actual integration of available data. Data fusion is the process of fusing multiple records representing the same real-world object into a single, consistent, and clean representation.
This article discusses why business intelligence is often too closely associated with data warehousing and should be replaced by a concept such as decision intelligence, which could be considered a modern version of earlier decision support systems.
J. Llinas, C. Bowman, G. Rogova, A. Steinberg, and F. White. In P. Svensson and J. Schubert (Eds.), Proceedings of the Seventh International Conference on Information Fusion (FUSION 2004, page 1218--1230. (2004)
R. Bruckner, B. List, and J. Schiefer. DaWaK 2000: Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery, page 317--326. London, UK, Springer-Verlag, (2002)
D. Rosca, S. Greenspan, M. Feblowitz, and C. Wild. Requirements Engineering, 1997., Proceedings of the Third IEEE International Symposium on, (January 1997)
M. Golfarelli, S. Rizzi, and I. Cella. DOLAP '04: Proceedings of the 7th ACM international workshop on Data warehousing and OLAP, page 1--6. New York, NY, USA, ACM Press, (2004)