
Abstract: Stream processing is about processing continuous streams of data by programs in a workflow. Continuous execution is discretized by grouping input stream tuples into batches and using one batch at a time for the execution of programs. The programs may generate stream data which may be input to subsequent programs in the workflow. They may also read as well as write some data in persistent store. Continuous queries are processed in the workflow. A continuous query (CQ) consists of a sequence of one time queries. There is a general agreement that each CQ normally spans over several batches of stream inputs. Apart from this, different notions of CQs exist in the literature, their one time queries ranging from just transactions (that is, single executions of programs) to composite transactions. In this paper, we look at how CQs can be defined generically and propose a correctness criterion for concurrent executions of CQs.
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| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
