Parallel analytics as a service

Wong, Petrie; He, Zhian; Lo, Eric

doi:10.1145/2463676.2463714

Cited by 20 publications

(12 citation statements)

References 20 publications

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“…Most of existing works on cloud databases focus on per-formance issues, such as live migration [13,14], workload consolidation [9,35], resource management [12], and virtualization [31]. However, data security has not been satisfactorily addressed so far.…”

Section: Introductionmentioning

confidence: 99%

Secure query processing with data interoperability in a cloud database environment

Wong

Kao

Cheung

et al. 2014

Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data

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We address security issues in a cloud database system which employs the DBaaS model. In such a model, a data owner (DO) exports its data to a cloud database service provider (SP). To provide data security, sensitive data is encrypted by the DO before it is uploaded to the SP. Existing encryption schemes, however, are only partially homomorphic in the sense that each of them was designed to allow one specific type of computation to be done on encrypted data. These existing schemes cannot be integrated to answer real practical queries that involve operations of different kinds. We propose and analyze a secure query processing system (SDB) on relational tables and a set of elementary operators on encrypted data that allow data interoperability, which allows a wide range of SQL queries to be processed by the SP on encrypted information. We prove that our encryption scheme is secure against two types of threats and that it is practically efficient.

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Section: Introductionmentioning

confidence: 99%

Secure query processing with data interoperability in a cloud database environment

Wong

Kao

Cheung

et al. 2014

Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data

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“…"Continuous Analytics as a Service (CaaaS) (E)" (2011), Chen et al [23] proposed providing Continuous Analytics as a Service (CaaaS) and implemented CaaaS as Software as a Service as well as Platform as a Service. "OLTP Database as a Service (M)" and Parallel Database as a Service (M) (2013), Wong et al [92] proposed it to support a large number of concurrent query executions after consolidation. "Content Delivery as a Service (CoDaaS) (E)" (2014), Jin et al [51] proposed it to distribute user generated content (UGC) in an efficient and economical fashion.…”

Section: Platform As a Servicementioning

confidence: 99%

“…"Big Data Platform as a Service (E)" (2012), Horey et al [45] proposed it. "MPP Database as a Service (MPPDBaaS) (E)" (2013), Wong et al [92] proposed that the offering of MPPDB-as-a-Service (MPPDBaaS) will become attractive for companies having analytical tasks on hundreds gigabytes to some ten terabytes of data. "Analysis as a Service (AaaS) (E)" (2014), Jingliang et al [52] proposed that the case Big Data uses Cloud Computing platform was called AaaS (Analysis as a Service).…”

Section: Cloud Service Meet Big Data and Internet Of Things Web Of Thmentioning

confidence: 99%

Sorting Terms of “aaS” of Everything as a Service

Duan¹,

Sun²,

Longo³

et al. 2016

IJNDC

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Numerous service models have been proposed in the form of "as a Service" or "aaS" in the past. This is especially eminent in the era of the Cloud under the term of anything as a service or everything as a service(XaaS). A unified view of XaaS can aid efficient classification of services for service registration, discovery, and composition. In view of that there lacks such a unified view which is demanded by systematic application of XaaS in the Web Service Ecosystem, towards proposing a taxonomy of "aaS", we present in this work a collection of "aaS" based on a throughout literature survey. We also proposed a strategy to identify the terms from the servicelization perspective.

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“…Descriptions of "Big Data" systems in production environments typically mention data sizes in the hundreds of TB to hundreds of PB [27,4,17] or trillions to hundreds of trillions of rows [12]. "Big Data" research studies on the other hand tend to work with much smaller datasets, ranging from hundreds of GB [6,18,16,24] to a few TBs [29,28,23,1]. The largest dataset we have seen used in an existing "Big Data" study is 16 TB [9].…”

Section: Related Workmentioning

confidence: 99%

IoTAbench

Arlitt

Marwah

Bellala

et al. 2015

Proceedings of the 6th ACM/SPEC International Conference on Performance Engineering

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The commoditization of sensors and communication networks is enabling vast quantities of data to be generated by and collected from cyber-physical systems. This "Internetof-Things" (IoT) makes possible new business opportunities, from usage-based insurance to proactive equipment maintenance. While many technology vendors now offer "Big Data" solutions, a challenge for potential customers is understanding quantitatively how these solutions will work for IoT use cases. This paper describes a benchmark toolkit called IoTAbench for IoT Big Data scenarios. This toolset facilitates repeatable testing that can be easily extended to multiple IoT use cases, including a user's specific needs, interests or dataset. We demonstrate the benchmark via a smart metering use case involving an eight-node cluster running the HP Vertica analytics platform. The use case involves generating, loading, repairing and analyzing synthetic meter readings. The intent of IoTAbench is to provide the means to perform "apples-to-apples" comparisons between different sensor data and analytics platforms. We illustrate the capabilities of IoTAbench via a large experimental study, where we store 22.8 trillion smart meter readings totaling 727 TB of data in our eight-node cluster.

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Parallel analytics as a service

Cited by 20 publications

References 20 publications

Secure query processing with data interoperability in a cloud database environment

Secure query processing with data interoperability in a cloud database environment

Sorting Terms of “aaS” of Everything as a Service

IoTAbench

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