Current research in indexing and mining time series data has produced many interesting algorithms and representations. However, it has not led to algorithms that can scale to the increasingly massive datasets encountered in science, engineering, and business domains. In this work, we show how a novel multiresolution symbolic representation can be used to index datasets which are several orders of magnitude larger than anything else considered in the literature. Our approach allows both fast exact search and ultra fast approximate search. We show how to exploit the combination of both types of search as sub-routines in data mining algorithms, allowing for the exact mining of truly massive real world datasets, containing millions of time series.

, in astronomy, multi-terabyte time series datasets are the norm. Most current algorithms faced with data which cannot fit in main memory resort to multiple scans of the disk/tape and are thus intractable. In this work we show how one particular definition of unusual time series, the time series discord, can

We present the cgmOLAP server, the first fully functional parallel OLAP system able to build data cubes at a rate of more than 1 Terabyte per hour. cgmOLAP incorporates a variety of novel approaches for the parallel computation of full cubes, partial cubes, and iceberg cubes as well as new parallel

Abstract. In this poster we describe the development of a distributed search engine, referred to as Físréal, which utilises inexpensive workstations, yet attains fast retrieval performance for Terabyte-sized collections. We also discuss the process of leveraging additional meaning from

The Terabyte Retrieval Track of the Text REtrieval Conference (TREC) provides an opportunity to test retrieval techniques and evaluation methodologies in the context of a terabyte-scale corpus. Given the size of the corpus, the track also provides a vehicle for participants to investigate query

track, we conducted a range of experiments investigating the effects of larger collections. Our main findings can be summarized as follows. First, we tested whether our retrieval system scales up to terabyte-scale collections. We found that our retrieval system can handle 25 million documents

The size of data sets being collected and analyzed in the industry for business intelligence is growing rapidly, mak- ing traditional warehousing solutions prohibitively expen- sive. Hadoop [3] is a popular open-source map-reduce im- plementation which is being used as an alternative to store

Cloud/Grid computing is envisioned to be a predominant computing model of the future. The movement of files between cloud and client is intrinsic to this model. With the creation of ever expanding data sets, the sizes of files have increased dramatically. Consequently, terabyte file transfers

loads with partial replication on a terabyte text database. We further investigate query locality with respect to time, replica size, and replica updating costs using real logs from THOMAS and Excite, and discuss the sensitivity of our results to these sample points.

developments in commodity hardware. We report on tests of redundant arrays of integrated drive electronics (IDE) disk drives for use in offline high energy physics data analysis. IDE redundant array of inexpensive disks (RAID) prices now are less than the cost per terabyte of million-dollar tape robots