440 research outputs found

    Data consistency: toward a terminological clarification

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    The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-21413-9_15Consistency is an inconsistency are ubiquitous term in data engineering. Its relevance to quality is obvious, since consistency is a commonplace dimension of data quality. However, connotations are vague or ambiguous. In this paper, we address semantic consistency, transaction consistency, replication consistency, eventual consistency and the new notion of partial consistency in databases. We characterize their distinguishing properties, and also address their differences, interactions and interdependencies. Partial consistency is an entry door to living with inconsistency, which is an ineludible necessity in the age of big data.Decker and F.D. Muñoz—supported by the Spanish MINECO grant TIN 2012-37719-C03-01.Decker, H.; Muñoz Escoí, FD.; Misra, S. (2015). Data consistency: toward a terminological clarification. 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    Normalizing Database Normalization Definitions In AIS Text Books

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    Due to the abstract nature of the definitions for normal forms, over the years the interpretations of the definitions published in the textbooks, both MIS and AIS disciplines, have been differentiated and even deviated from its original form. The concept of deviation from the original form is a phenomenon that linguists call “semantic drift.” The most noticeable deviations are on first and second normal forms (i.e., 1NF and 2NF). Their definitions range from “atomic attribute” to “removing repeating group” for 1NF and from “functional dependency” to “removing partial dependency” in addition to being 1NF for 2NF. The purpose of this paper is to compare definitions of first, second, and third normal forms from the textbooks with those of the earlier forms and to identify shortfalls if there are any

    Transaction processing in consistency-aware user’s applications deployed on NoSQL databases

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    NoSQL databases are capable of storing and processing big data which is characterized by various properties such as volume, variety and velocity. Such databases are used in a variety of user applications that need large volume of data which is highly available and efficiently accessible. But they do not enforce or require strong data consistency nor do they support transactions. This paper investigates into the transaction processing in consistency-aware applications hosted on MongoDB and Riak which are two representatives of Document and Key-Value NoSQL databases, respectively. It develops new transaction schemes in order to provide NoSQL databases with transactional facilities as well as to analyze the effects of transactions on data consistency and efficiency in user’s applications. The proposed schemes are evaluated using the YCSB + T benchmark which is based on Yahoo! Cloud Services Benchmark (YCSB). Experimental results show that using the proposed schemes, strong consistency can be achieved in MongoDB and Riak without severely affecting their efficiency. We also conduct experiments in order to analyse the level of consistencyof MongoDB and Riak transactional systems

    Comparison of Graph Databases and Relational Databases When Handling Large-Scale Social Data

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    Over the past few years, with the rapid development of mobile technology, more people use mobile social applications, such as Facebook, Twitter and Weibo, in their daily lives, and there is an increasing amount of social data. Thus, finding a suitable storage approach to store and process the social data, especially for the large-scale social data, should be important for the social network companies. Traditionally, a relational database, which represents data in terms of tables, is widely used in the legacy applications. However, a graph database, which is a kind of NoSQL databases, is in a rapid development to handle the growing amount of unstructured or semi-structured data. The two kinds of storage approaches have their own advantages. For example, a relational database should be a more mature storage approach, and a graph database can handle graph-like data in an easier way. In this research, a comparison of capabilities for storing and processing large-scale social data between relational databases and graph databases is applied. Two kinds of analysis, the quantitative research analysis of storage cost and executing time and the qualitative analysis of five criteria, including maturity, ease of programming, flexibility, security and data visualization, are taken into the comparison to evaluate the performance of relational databases and graph databases when handling large-scale social data. Also, a simple mobile social application is developed for experiments. The comparison is used to figure out which kind of database is more suitable for handling large-scale social data, and it can compare more graph database models with real-world social data sets in the future research

    Expected utility theory, Jeffrey’s decision theory, and the paradoxes

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    In Richard Bradley’s book, Decision Theory with a Human Face, we have selected two themes for discussion. The first is the Bolker-Jeffrey theory of decision, which the book uses throughout as a tool to reorganize the whole field of decision theory, and in particular to evaluate the extent to which expected utility theories may be normatively too demanding. The second theme is the redefinition strategy that can be used to defend EU theories against the Allais and Ellsberg paradoxes, a strategy that the book by and large endorses, and even develops in an original way concerning the Ellsberg paradox. We argue that the BJ theory is too specific to fulfil Bradley’s foundational project and that the redefinition strategy fails in both the Allais and Ellsberg cases. Although we share Bradley’s conclusion that EU theories do not state universal rationality requirements, we reach it not by a comparison with BJ theory, but by a comparison with the non-EU theories that the paradoxes have heuristically suggested

    4th SC@RUG 2007 proceedings:Student Colloquium 2006-2007

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