UpStream: storage-centric load management for streaming applications with update semantics

Moga, Alexandru; Botan, Irina; Tatbul, Nesime

doi:10.1007/s00778-011-0229-7

Cited by 9 publications

(4 citation statements)

References 23 publications

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“…Furthermore, with reordering transactions may see a stale view of the database that is different from what they would observe in the original order. For many applications such as stock trading [65], manufacturing [66] and warehouses [67], transactions are time-sensitive and stale reads are tolerated only when read values have bounded staleness.…”

Section: Implications Of Transaction Reorderingmentioning

confidence: 99%

Competitive Consistent Caching for Transactions

Cao

Zhao

2022

2022 IEEE 38th International Conference on Data Engineering (ICDE)

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This paper studies cache policies for transactional caches. Different from conventional caches that focus on latency, transactional caches are primarily used to augment database systems and improve their transaction throughput by offloading read load onto the cache. A read transaction commits on the cache only if it is a consistent cache hit, i.e., all of its reads see a consistent view of the database. We prove that conventional cache policies are not competitive for transactions. We then show that for the large class of batching-based transaction systems, one can break the theoretical performance barrier of conventional cache policies via transaction consistency aware cache policies, although it is NP-complete to find the optimal ones. As a proof, we develop a consistent cache policy that is theoretically competitive under common cache schemes. To further exploit batching, we propose to reorder transactions within batches while guaranteeing that each transaction sees data values with bounded staleness. Using benchmarks and real-life workloads, we experimentally verify that our policy improves the transaction throughput of Memcached atop HBase by 126.95% on average, up to 479.27% higher than existing cache policies adopted for transactions.

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Section: Implications Of Transaction Reorderingmentioning

confidence: 99%

Competitive Consistent Caching for Transactions

Cao

Zhao

2022

2022 IEEE 38th International Conference on Data Engineering (ICDE)

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“…Every incoming tuple may not be new tuples, some may be revision which are not same as normal updates, and they are the corrections that invalidate old tuple or knowledge and incorporate new knowledge [14,19].To improve the performance, Alexandru et al [1] proposed the framework for managing the data that is storage-centric. ParisaHaghani et al [23] proposed top-k query processing, and improved model ABS [10].…”

Section: Related Workmentioning

confidence: 99%

Mining Closed Item sets from Tuple-Evolving Data Streams

Peddireddy*,

Anuradha,

Murthy

2019

IJEAT

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Frequent Itemset Mining is playing major role in extracting useful knowledge from data streams that are exhibiting high data flow. Studies in data streams shows that every incoming data is considered as new tuple which is considered as revised tuple in some applications called as tuple evolving data streams. Extracting redundant less knowledge from such kind of application helps in better decision making with new challenges.One of the issue is, due to incoming revised tuple, some of the frequent itemsets may turn to infrequent or previously ignore itemsets may become frequent. Other issue is result of FIM may be huge and redundant results.In this paper, we address solution to the problem by finding closed itemsets from tuple revision data streams. We propose an efficient approach MCST that uses compressed SlideTree data structure to maintain stream data,proposeHIS hash tableto maintain itemsets, and CIS tables to maintain closed id sets to improve search performance of HIS.

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“…To minimize the staleness of query results over streams with revision tuples, Alexandru Moga et al proposed an efficient storage-centric framework for load management over the streams [13].…”

Section: Related Workmentioning

confidence: 99%

Mining frequent itemsets over tuple-evolving data streams

Zhang

Mazuran

Mousavi

et al. 2013

Proceedings of the 28th Annual ACM Symposium on Applied Computing

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In many data streaming applications today, tuples inside the streams may get revised over time. This type of data stream brings new issues and challenges to the data mining tasks. We present a theoretical analysis for mining frequent itemsets from sliding windows over such data. We define conditions that determine whether an infrequent itemset will become frequent when some existing tuples inside the streams have been updated. We design simple but effective structures for managing both the evolving tuples and the candidate frequent itemsets. Moreover, we provide a novel verification method that efficiently computes the counts of candidate itemsets. Experiments on real-world datasets show the efficiency and effectiveness of our proposed method.

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UpStream: storage-centric load management for streaming applications with update semantics

Cited by 9 publications

References 23 publications

Competitive Consistent Caching for Transactions

Competitive Consistent Caching for Transactions

Mining Closed Item sets from Tuple-Evolving Data Streams

Mining frequent itemsets over tuple-evolving data streams

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