The CQL continuous query language: semantic foundations and query execution

By Arvind Arasu, Shivnath Babu, Jennifer Widom. The VLDB journal, 2006

1,382 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/f9c533f05d9ee12b397c4b39010ded0db42a7d86

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Citations in the corpus

Language-Directed Hardware Design for Network Performance Monitoring (2017, background): “We did not find a need for more general joins akin to joins in streaming query languages like CQL [30].”
High-availability algorithms for distributed stream processing (2005, background): “Figure 3 depicts the containment relationship among these operator types and the classification of Aurora operators [1, 2].”
A Native and Adaptive Approach for Unified Processing of Linked Streams and Linked Data (2011, methodology): “Similarly to the relation-to-stream operator of CQL [5], we define an operator, based on some patterns, to generate RDF streams from result sets.”
No pane, no gain: efficient evaluation of sliding-window aggregates over data streams (2005, background): “Since each input tuple belongs to multiple windows, such approaches buffer a tuple until it is processed for the aggregate over the last window to which it belongs.”
Towards Expressive Publish/Subscribe Systems (2006, result): “Like SQL itself, CQL is declarative and admits of a formal specification [6]; and there are some initial results characterizing a sub-class of queries that can be computed with bounded memory [28, 5].”
Efficient Computation of Frequent and Top-k Elements in Data Streams (2005, background): “Currently, data stream management systems are used for monitoring click streams [31], stock tickers [11, 46], sensor readings [7], telephone call records [15], network packet traces [17], auction bidding patterns [2], traffic management [3], network-aware clustering [12], and security against DoS [1”
MacroBase: Analytic Monitoring for the Internet of Things (2016, methodology): “MacroBase’s interfaces using an object-oriented interface, reﬂecting their current implementation. However, each of MacroBase’s operator types is compatible with existing stream-to-relation semantics [9], theoretically allowing additional relational and stream-based processing between stages. Realiz”
SPADE: the system s declarative stream processing engine (2008, background): “For example, database engineers typically conceive their applications in terms of the operators available in the stream relational algebra [5, 23].”
MacroBase: Prioritizing Attention in Fast Data (2016, background): “However, each of MacroBase’s operator types is compatible with existing stream-to-relation semantics [9], theoretically allowing additional relational and stream-based processing between stages.”
Frenetic: a network programming language (2011, background): “At a high level, Frenetic is also similar to streaming languages such as StreamIt [39], CQL [7], Esterel [8], Brooklet [38], etc.”
Declarative Support for Sensor Data Cleaning (2006, methodology): “5 In ESP, we use CQL [ 8 ] as our declarative language as we have a data stream system, Tele-”
Fault-tolerance in the Borealis distributed stream processing system (2005, background): “Much work has been done on data models and operators [1, 6, 16, 28, 41], efficient processing [7, 8, 12, 30], and resource management [13, 17, 30, 35, 38] for SPEs.”
C-SPARQL: a Continuous Query Language for RDF Data Streams (2010, methodology): “This research evolved into the specification and development of a query language tailored for data streams, named CQL ~ 3].”