The Claremont report on database research

Agrawal, Rakesh; Ailamaki, Anastasia; Bernstein, Philip A.; Brewer, Eric; Carey, Michael J.; Chaudhuri, Surajit; Doan, AnHai; Florescu, Daniela; Franklin, Michael J.; García-Molina, Héctor; Gehrke, Johannes; Gruenwald, Le; Haas, Laura M.; Halevy, Alon; Hellerstein, Joseph M.; Ioannidis, Yannis; Korth, Hank; Kossmann, Donald; Madden, Samuel; Magoulas, Roger; Ooi, Beng Chin; O’Reilly, Tim; Ramakrishnan, Raghu; Sarawagi, Sunita; Stonebraker, Michael; Szalay, Alexander S.; Weikum, Gerhard

doi:10.1145/1516046.1516062

Cited by 65 publications

(22 citation statements)

References 6 publications

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“…Past meetings were held in 1989 [BDD+89], 1990 [SSU91], 1995 [SSU96], 1996 [SZ96], 1998 [BBC+98], 2003 [AAB+05], and 2008 [AAB+09]. Continuing this tradition, twentyeight database researchers and two invited speakers met in October 2013 at the Beckman Center on the University of California-Irvine campus for two days of discussions (see http://beckman.cs.wisc.edu).…”

Section: Introductionmentioning

confidence: 99%

The Beckman Report on Database Research

et al. 2014

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Every few years a group of database researchers meets to discuss the state of database research, its impact on practice, and important new directions. This report summarizes the discussion and conclusions of the eighth such meeting, held October 14-15, 2013 in Irvine, California. It observes that Big Data has now become a defining challenge of our time, and that the database research community is uniquely positioned to address it, with enormous opportunities to make transformative impact. To do so, the report recommends significantly more attention to five research areas: scalable big/fast data infrastructures; coping with diversity in the data management landscape; end-to-end processing and understanding of data; cloud services; and managing the diverse roles of people in the data life cycle.

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Section: Introductionmentioning

confidence: 99%

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“…This thesis addresses the problem of efficient event processing over data streams by proposing a solution that consists of three layers 1 : the higher-, the core-, and the lower-layers. At the core-layer of our solution lies an efficient Boolean expression matching kernel [20,8,21,22].…”

Section: Thesis Outlinementioning

confidence: 99%

“…The extensibility of this matching kernel 2 gives rise to the higher-layer of our solution that supports application requirements including top-k processing [22], XML filtering and dissemination [20], and (complex) event processing [8]. Another major benefit of our matching kernel is its regular, more tabular or- 1 As part of this thesis, we also developed a Boolean expression workload generator (BEGen): http://msrg.org/datasets/BEGen 2 The matching kernel is implemented in C (47K lines of code. )…”

Section: Thesis Outlinementioning

confidence: 99%

“…In user-centric processing applications, there are computational advertising [28,10], online job sites [17,28], and location-based services for emerging applications in the co-spaces [1,19]; common to all are patterns and specifications (e.g., advertising campaigns, job profiles, service descriptions) modeled as Boolean expressions, XPath expressions, or SQL queries and incoming user information (e.g., user profiles and preferences) modeled as events using attribute-value pairs, XML document, or relational tuples. In the real-time analysis domain, there are (complex) event processing [11,2,6,7,5], XML filtering [3,18,15], intrusion detection [27], and computational finance [23]; again, common among these applications are predefined set of patterns (e.g., investment strategies and attack specifications) modeled as subscriptions and streams of incoming data (e.g., XML documents, data packets, stock feeds) modeled as events.…”

Section: Introductionmentioning

confidence: 99%

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Towards an extensible efficient event processing kernel

Sadoghi

2012

Proceedings of the on SIGMOD/PODS 2012 PhD Symposium

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The efficient processing of large collections of patterns (Boolean expressions, XPath expressions, or continuous SQL queries) over data streams plays a central role in major data intensive applications ranging from user-centric processing and personalization to realtime data analysis. On the one hand, emerging user-centric applications, including computational advertising and selective information dissemination, demand determining and presenting to an enduser only the most relevant content that is both user-consumable and suitable for limited screen real estate of target (mobile) devices. We achieve these user-centric requirements through novel highdimensional indexing structures and (parallel) algorithms. On the other hand, applications in real-time data analysis, including computational finance and intrusion detection, demand meeting stringent subsecond processing requirements and providing high-frequency and low-latency event processing over data streams. We achieve real-time data analysis requirements by leveraging reconfigurable hardware -FPGAs -to sustain line-rate processing by exploiting unprecedented degrees of parallelism and potential for pipelining, only available through custom-built, application-specific, and low-level logic design. Finally, we conduct a comprehensive evaluation to demonstrate the superiority of our proposed techniques in comparison with state-of-the-art algorithms designed for event processing.

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“…In light of an increasing concern about energy, energy management starts to draw attention of the database community. The 2008 Claremont report on database research emphasized explicitly the importance of power-aware DBMSs that limit energy costs without sacrificing scalability [4]. To achieve energy-efficient DBMSs, solutions from both hardware and software perspectives are desirable.…”

Section: Energy-efficient Data Manage-mentmentioning

confidence: 99%

A survey on energy-efficient data management

et al. 2011

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Energy management has now become a critical and urgent issue in green computing. A lot of efforts have been made on energy-efficiency computing at various levels from individual hardware components, system software, to applications. In this paper, we describe the energyefficiency computing problem, as well as possible strategies to tackle the problem. We survey some recently developed energy-saving data management techniques. Benchmarks and power models are described in the end for the evaluation of energy-efficiency solutions.

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The Claremont report on database research

Abstract: Database research is expanding, with major efforts in system architecture, new languages, cloud services, mobile and virtual worlds, and interplay between structure and text.

Cited by 65 publications

References 6 publications

The Beckman Report on Database Research

The Beckman Report on Database Research

Towards an extensible efficient event processing kernel

A survey on energy-efficient data management

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