XQuery reloaded

Bamford, Roger; Borkar, Vinayak; Brantner, Matthias; Fischer, Peter M.; Florescu, Daniela; Graf, David; Kossmann, Donald; Kraska, Tim; Mureşan, Dan Ioan; Nasoi, Sorin; Zacharioudakis, Markos

doi:10.14778/1687553.1687560

Cited by 23 publications

(6 citation statements)

References 26 publications

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“…In addition, it provides the basic infrastructure to group data into different "spaces" (e.g. wellness, fitness, outcomes, medical or health records) and to share data within "circles" [18,19]. For instance, a user can add a doctor to the user's "medical" space, thereby allowing the doctor to see all of the user's medical records.…”

Section: The Hdc Structurementioning

confidence: 99%

Health Data Cooperatives – Citizen Empowerment

Hafen¹,

Kossmann²,

Brand³

2014

Methods Inf Med

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SummaryIntroduction: This article is part of a Focus Theme of Methods of Information in Medicine on Health Record Banking.Background: Healthcare is often ineffective and costs are steadily rising. This is in a large part due to the inaccessibility of medical and health data stored in multiple silos. Further -more, in most cases molecular differences between individuals that result in different susceptibilities to drugs and diseases as well as targeted interventions cannot be taken into account. Technological advances in genome sequencing and the interaction of ’omics’ data with environmental data on one hand and mobile health on the other, promise to generate the longitudinal health data that will form the basis for a more personalized, precision medicine.Objectives: For this new medicine to become a reality, however, millions of personal health data sets have to be aggregated. The value of such aggregated personal data has been recognized as a new asset class and many commercial entities are competing for this new asset (e.g. Google, Facebook, 23andMe, PatientsLikeMe). The primary source and beneficiary of personal health data is the individual. As a collective, society should be the beneficiary of both the eco -nomic and health value of these aggregated data and (health) information.Methods: We posit that empowering citi -zens by providing them with a platform to safely store, manage and share their health-related data will be a necessary element in the transformation towards a more effective and efficient precision medicine. Such health data platforms should be organized as co -operatives that are solely owned and controlled by their members and not by shareholders. Members determine which data they want to share for example with doctors or to contribute to research for the benefit of their health and that of society. Members will also decide how the revenues generated by granting third parties access to the anonymized data that they agreed to share, should be invested in research, information or education.Results: Currently no functional Health Data Cooperatives exist yet. The relative success of health data repositories such as 23andme and PatientsLikeMe indicates that citizens are willing to participate in research even if – and in contrast to the cooperative model – the commercial value of these data does not go back to the collective of users.Conclusions: In the Health Data Cooperative model, the citizens with their data would take the center stage in the healthcare system and society would benefit from the health-related and financial benefits that aggregation of these data brings.

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Section: The Hdc Structurementioning

confidence: 99%

Health Data Cooperatives – Citizen Empowerment

Hafen¹,

Kossmann²,

Brand³

2014

Methods Inf Med

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“…We compare our system against two 16 of the fastest academic and industrial engines: MonetDB/XQuery [6] and Zorba [4]. First, we used several sequence queries on Nasdaq transactions (embedded in XML tags), including the 'V'-shape pattern (defined in Example 15 and Query 16).…”

Section: Sequence Queries Vs Xpath Enginesmentioning

confidence: 99%

High-performance complex event processing over XML streams

Mozafari

Zeng

Zaniolo

2012

Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data

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Much research attention has been given to delivering high-performance systems that are capable of complex event processing (CEP) in a wide range of applications. However, many current CEP systems focus on efficiently processing data having a simple structure, and are otherwise limited in their ability to efficiently support complex continuous queries on structured or semi-structured information. However, XML streams represent a very popular form of data exchange, comprising large portions of social network and RSS feeds, financial records, configuration files, and similar applications requiring advanced CEP queries. In this paper, we present the XSeq language and system that support CEP on XML streams, via an extension of XPath that is both powerful and amenable to an efficient implementation. Specifically, the XSeq language extends XPath with natural operators to express sequential and Kleene-* patterns over XML streams, while remaining highly amenable to efficient implementation. XSeq is designed to take full advantage of recent advances in the field of automata on Visibly Pushdown Automata (VPA), where higher expressive power can be achieved without compromising efficiency (whereas the amenability to efficient implementation was not demonstrated in XPath extensions previously proposed).We illustrate XSeq's power for CEP applications through examples from different domains, and provide formal results on its expressiveness and complexity. Finally, we present several optimization techniques for XSeq queries. Our extensive experiments indicate that XSeq brings outstanding performance to CEP applications: two orders of magnitude improvement are obtained over the same queries executed in general-purpose XML engines.

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“…Bamford et al . describe a number of XQuery‐related projects and application scenarios. They also give an overview of XQuery implementation techniques, and review available XQuery implementations as of 2009.…”

Section: Related Workmentioning

confidence: 99%

Using XQuery for problem solving

Kilpeläinen

2011

Softw Pract Exp

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SUMMARY XQuery is a recommendation by the World Wide Web Consortium as a standard XML query language. In addition to being a special‐purpose query language, XQuery has also features that support unexpected applications such as problem solving. We demonstrate and discuss these features by presenting several XQuery programs for solving recreational problems and puzzles. Copyright © 2011 John Wiley & Sons, Ltd.

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XQuery reloaded

Cited by 23 publications

References 26 publications

Health Data Cooperatives – Citizen Empowerment

Health Data Cooperatives – Citizen Empowerment

High-performance complex event processing over XML streams

Using XQuery for problem solving

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