Query Languages and Data Models for Database Sequences and Data Streams

Law, Yan-Nei; Wang, Haixun; Zaniolo, Carlo

doi:10.1016/b978-012088469-8.50045-0

Cited by 90 publications

(46 citation statements)

References 7 publications

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“…The first impact is Turing completeness, something that was proposed from a theoretical basis on [3]. Some event languages have a solid background in event calculus and result in one line expressions that may be compact implementations but are not apt to user optimizations -apart from physical query plans.…”

Section: The Impact On Complex Event Processingmentioning

confidence: 99%

“…A user-defined aggregate function consists of three parts: an initialization function that defines (local) state, opening a window within which the computation takes place; an iteration function that updates state; and a termination function that returns state, when the window closes. User-defined aggregates have been proven to be a sufficient extension to SQL for modeling complex patterns over data streams as finite state machines [3].…”

Section: Related Workmentioning

confidence: 99%

“…The continuous semantics of queries were first defined in Tapestry [1]. Roughly speaking, a continuous query is a monotonic query -or, equivalently, a nonblocking query [3] -that yields incremental results over a sliding time window whose duration is defined by the current execution time and the last timestamp observed in the previous result set. Figure 1 shows a variant of the basic continuous query execution model, as proposed in [1].…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Unification of Publish/Subscribe Systems and Stream Databases

Sventek

Koliousis

2012

Lecture Notes in Computer Science

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Abstract. There is increasing demand for complex event processing of ever-expanding volumes of data in an ever-growing number of application domains. Traditional complex event processing technologies, based upon stream database management systems or publish/subscribe systems, are adept at handling many of these technologies. A growing number of hybrid complex event detection scenarios require features of both technologies. This paper describes a unification of publish/subscribe and stream database concepts to tackle all complex event processing scenarios, with particular emphasis upon hybrid scenarios. The paper describes the architecture for this unified system, the automaton programming language that it supports, and the run-time system that animates automata. Several examples of automata that exploit the system's unified nature are discussed. Raw automata performance is characterised, and its relative performance against Cayuga with respect to stock trend analysis is presented.

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Section: The Impact On Complex Event Processingmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Unification of Publish/Subscribe Systems and Stream Databases

Sventek

Koliousis

2012

Lecture Notes in Computer Science

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“…Realtime and active databases can perform online relational queries, but do not scale to such highrate inputs or queries. Stream databases such as [9], [22] offer powerful query languages with sliding windows and sequence operators but also do not scale to high data rate.…”

Section: Related Workmentioning

confidence: 99%

Towards hybrid online on-demand querying of realtime data with stateful complex event processing

Zhou

Simmhan

Prasanna

2013

2013 IEEE International Conference on Big Data

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Abstract-Emerging Big Data applications in areas like ecommerce and energy industry require both online and ondemand queries to be performed over vast and fast data arriving as streams. These present novel challenges to Big Data management systems. Complex Event Processing (CEP) is recognized as a high performance online query scheme which in particular deals with the velocity aspect of the 3-V's of Big Data. However, traditional CEP systems do not consider data variety and lack the capability to embed ad hoc queries over the volume of data streams. In this paper, we propose H2O, a stateful complex event processing framework, to support hybrid online and on-demand queries over realtime data. We propose a semantically enriched event and query model to address data variety. A formal query algebra is developed to precisely capture the stateful and containment semantics of online and on-demand queries. We describe techniques to achieve the interactive query processing over realtime data featured by efficient online querying, dynamic stream data persistence and on-demand access. The system architecture is presented and the current implementation status reported.

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“…But mostly those efforts were for relational or object-oriented models [23], and can be considered as a conceptual background to solve advanced data management challenges [24]. The emerging applications, such as sensor data [25], Internet traffic [26], financial tickers [27,28] and e-commerce [29], produce large volumes of timestamped data continuously in real-time [30,31]. The current methods of centralized or distributed storage with static data impose constraints in addressing the real-time requirements [32], as they inflict pre-defined time convictions unless timestamped attributes are explicitly added [31].…”

Section: Introductionmentioning

confidence: 99%

Modeling temporal aspects of sensor data for MongoDB NoSQL database

2017

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Proliferation of structural, semi-structural and no-structural data, has challenged the scalability, flexibility and processability of the traditional relational database management systems (RDBMS). The next generation systems demand horizontal scaling by distributing data over autonomously addable nodes to a running system. For schema flexibility, they also want to process and store different data formats along the sequence factor in the data. NoSQL approaches are solutions to these, hence big data solutions are vital nowadays. But in monitoring scenarios sensors transmit the data continuously over certain intervals of time and temporal factor is the main property of the data. Therefore the key research aspect is to investigate schema flexibility and temporal data integration aspects together. We need to know that: what data modelling should we adopt for a data driven real-time scenario; that we could store the data effectively and evolve the schema accordingly during data integration in NoSQL environments without losing big data advantages. In this paper we explain a middleware based schema model to support the temporal oriented storage of real-time data of ANT+ sensors as hierarchical documents. We explain how to adopt a schema for the data integration by using an algorithm based approach for flexible evolution of the model for a document oriented database, i.e, MongoDB. The proposed model is logical, compact for storage and evolves seamlessly upon new data integration

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Query Languages and Data Models for Database Sequences and Data Streams

Cited by 90 publications

References 7 publications

Unification of Publish/Subscribe Systems and Stream Databases

Unification of Publish/Subscribe Systems and Stream Databases

Towards hybrid online on-demand querying of realtime data with stateful complex event processing

Modeling temporal aspects of sensor data for MongoDB NoSQL database

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