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At UC Irvine, we are building a next generation parallel database system, called ASTERIX, as our approach to addressing today's " Big Data " management challenges. ASTERIX aims to combine time-tested principles from parallel database systems with those of the Web-scale computing community, such as fault tolerance for long running jobs. In this demo, we(More)
Social networks, online communities, mobile devices, and instant messaging applications generate complex, unstructured data at a high rate, resulting in large volumes of data. This poses new challenges for data management systems that aim to ingest, store, index , and analyze such data efficiently. In response, we released the first public version of(More)
AsterixDB is a new, full-function BDMS (Big Data Management System) with a feature set that distinguishes it from other platforms in today's open source Big Data ecosystem. Its features make it well-suited to applications like web data warehousing, social data storage and analysis, and other use cases related to Big Data. Aster-ixDB has a flexible NoSQL(More)
A growing wealth of digital information is being generated on a daily basis in social networks, blogs, online communities, etc. Organizations and researchers in a wide variety of domains recognize that there is tremendous value and insight to be gained by warehousing this emerging data and making it available for querying, analysis, and other purposes. This(More)
In recent years, many websites have started providing keyword-search services on maps. In these systems, users may experience difficulties finding the entities they are looking for if they do not know their exact spelling, such as the name of a restaurant. In this paper, we present a solution to support fuzzy keyword search on spatial data. We combine a(More)
Large quantities of raw data are being generated by many different sources in different formats. Private and public sectors alike acclaim the valuable information and insights that can be mined from such data to better understand the dynamics of everyday life, such as traffic, worldwide logistics, and social behavior. For this reason, storing, managing, and(More)
With the social-media data explosion, near real-time queries, particularly those of a spatio-temporal nature, can be challenging. In this paper, we show how to efficiently answer queries that target recent data within very large data sets. We describe a solution that exploits a natural partitioning property that LSM-based indexes have for components,(More)
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