SCOPE: parallel databases meet MapReduce

Zhou, Jingren; Bruno, Nicolas; Wu, Ming-Chuan; Larson, Per-Åke; Chaiken, Ronnie; Shakib, Darren

doi:10.1007/s00778-012-0280-z

Cited by 124 publications

(91 citation statements)

References 25 publications

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“…And (iii) hybrid/integrated multistore systems, whose objective is to query indifferently structured and unstructured data using a SQL-like declarative language. SCOPE system [67] and CoherentPaaS project [7], illustrate this approach.…”

Section: Cloud Systemsmentioning

confidence: 99%

“…Furthermore, the alternative which suggests writing a program with low level languages requires a high expertise level from the user. For these reasons, Hive [59,60], SCOPE [67] systems, and CoherentPaaS project [7] propose to use declarative languages close to SQL language. These languages allow a user to define a relation compounded of several typed columns.…”

Section: Data Manipulation Languages Using Relational Operatorsmentioning

confidence: 99%

“…As for SCOPE system [67], the optimization process includes a logical optimization and chooses for each operator the best algorithm to process it (e.g. hash join or sort-merge join) and determines the scheduling of operators.…”

Section: Data Manipulation Languages Using Relational Operatorsmentioning

confidence: 99%

“…hash join or sort-merge join) and determines the scheduling of operators. With regard to avoid data reshuffling, which deteriorates the performance, a top-down approach is proposed in order that a parent operator imposes its requirements to the child operators [3,66,67].…”

Section: Data Manipulation Languages Using Relational Operatorsmentioning

confidence: 99%

“…Their common goal is to use the benefits of data independence and implicit (automatic) optimization programs of parallel database approaches and the advantages of scalability, fault tolerance and elasticity of cloud systems. Thus, high level of declarative languages have emerged HiveQL [60], SCOPE language [67], and CloudMdsQL [7]. This allows an automatic optimization-parallelization of queries.…”

Section: Introductionmentioning

confidence: 99%

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Big Data Management in the Cloud: Evolution or Crossroad?

Hameurlain

Morvan

2016

Communications in Computer and Information Science

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. Abstract. In this paper, we try to provide a synthetic and comprehensive state of the art concerning big data management in cloud environments. In this perspective, data management based on parallel and cloud (e.g. MapReduce) systems are overviewed, and compared by relying on meeting software requirements (e.g. data independence, software reuse), high performance, scalability, elasticity, and data availability. With respect to proposed cloud systems, we discuss evolution of their data manipulation languages and we try to learn some lessons should be exploited to ensure the viability of the next generation of large-scale data management systems for big data applications.

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Section: Cloud Systemsmentioning

confidence: 99%

Section: Data Manipulation Languages Using Relational Operatorsmentioning

confidence: 99%

Section: Data Manipulation Languages Using Relational Operatorsmentioning

confidence: 99%

Section: Data Manipulation Languages Using Relational Operatorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Big Data Management in the Cloud: Evolution or Crossroad?

Hameurlain

Morvan

2016

Communications in Computer and Information Science

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Big Data with Cloud Computing: an insight on the computing environment, MapReduce, and programming frameworks

Fernández

Río

López

et al. 2014

WIREs Data Min & Knowl

192

107

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The term 'Big Data' has spread rapidly in the framework of Data Mining and Business Intelligence. This new scenario can be defined by means of those problems that cannot be effectively or efficiently addressed using the standard computing resources that we currently have. We must emphasize that Big Data does not just imply large volumes of data but also the necessity for scalability, i.e., to ensure a response in an acceptable elapsed time. When the scalability term is considered, usually traditional parallel-type solutions are contemplated, such as the Message Passing Interface or high performance and distributed Database Management Systems. Nowadays there is a new paradigm that has gained popularity over the latter due to the number of benefits it offers. This model is Cloud Computing, and among its main features we has to stress its elasticity in the use of computing resources and space, less management effort, and flexible costs. In this article, we provide an overview on the topic of Big Data, and how the current problem can be addressed from the perspective of Cloud Computing and its programming frameworks. In particular, we focus on those systems for large-scale analytics based on the MapReduce scheme of data are recorded everyday resulting in a large volume of information; this incoming information arrives at a high rate and its processing involves real-time requirements implying a high velocity; we may find a wide variety of structured, semi-structured, and unstructured data; and data have to be cleaned before the integration into the system in order to maintain veracity.1 This 4V property is one of the most widespread definitions of what is known as the Big Data problem, 2,3 which has become a hot topic of interest within academia and corporations. The current explosion of data that is being generated is due to three main reasons 4 : (1) hundreds of applications such as mobile sensors, social media services, and other related devices are collecting information continuously; (2) storage capacity has improved so much that collecting data is cheaper than ever, making preferable to buy more storage space rather than deciding what to delete; (3) Machine Learning and information retrieval approaches have reached a significant improvement in the last years, thus enabling the acquisition of a higher degree of knowledge from data. 5,6 Corporations are aware of these developments. Gaining critical business insights by querying and analyzing such massive amounts of data is becoming a necessity. This issue is known as Business Intelligence (BI), 7,8 which refers to decision support systems that combine data gathering, data storage, and knowledge management with analysis to provide input to the decision process. 9 Regarding the former issues, a new concept appears as a more general field, integrating data warehousing, Data Mining (DM), and data visualization for Business Analytics. This topic is known as Data Science. 10,11The data management and analytics carried out in conventional database systems (and other rela...

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