% This data is distributed under the terms of the Open Data Commons Attribution License (ODC-By) v1.0 - See more at: http://opendatacommons.org/licenses/by/1-0/
% All issues of Volume 1
% Volume 1, Issue 2, 2014
    @Article{OJDB-v1i2n01_Mortensen,
        title     = {Using Business Intelligence to Improve DBA Productivity},
        author    = {Eric A. Mortensen and
                     En Cheng},
        journal   = {Open Journal of Databases (OJDB)},
        issn      = {2199-3459},
        year      = {2014},
        volume    = {1},
        number    = {2},
        pages     = {1--16},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201705194595},
        urn       = {urn:nbn:de:101:1-201705194595},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {The amount of data collected and used by companies has grown rapidly in size over the last decade.  Business leaders are now using Business Intelligence (BI) systems to make effective business decisions against large amounts of data. The growth in the size of data has been a major challenge for Database Administrators (DBAs). The increase in the number and size of databases at the speed they have grown has made it difficult for DBA teams to provide the same level of service that the business requires they provide. The methods that DBAs have used in the last several decades can no longer be performed with the efficiency needed over all of the databases they administer. This paper presents the first BI system to improve DBA productivity and providing important data metrics for Information Technology (IT) managers. The BI system has been well received by Sherwin Williams Database Administrators.  It has i) enabled the DBA team to quickly determine which databases needed work by a DBA without manually logging into the system; ii) helped the DBA team and its management to easily answer other business users' questions without using DBAs' time to research the issue; and iii) helped the DBA team to provide the business data for unanticipated audit request.}
    }
    @Article{OJDB-v1i2n02_Abramova,
        title     = {Which NoSQL Database? A Performance Overview},
        author    = {Veronika Abramova and
                     Jorge Bernardino and
                     Pedro Furtado},
        journal   = {Open Journal of Databases (OJDB)},
        issn      = {2199-3459},
        year      = {2014},
        volume    = {1},
        number    = {2},
        pages     = {17--24},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201705194607},
        urn       = {urn:nbn:de:101:1-201705194607},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {NoSQL data stores are widely used to store and retrieve possibly large amounts of data, typically in a key-value format. There are many NoSQL types with different performances, and thus it is important to compare them in terms of performance and verify how the performance is related to the database type. In this paper, we evaluate five most popular NoSQL databases: Cassandra, HBase, MongoDB, OrientDB and Redis. We compare those databases in terms of query performance, based on reads and updates, taking into consideration the typical workloads, as represented by the Yahoo! Cloud Serving Benchmark. This comparison allows users to choose the most appropriate database according to the specific mechanisms and application needs.}
    }
% Volume 1, Issue 1, 2014
    @Article{OJDB-v1i1n01_Grandi,
        title     = {Introductory Editorial},
        author    = {Fabio Grandi},
        journal   = {Open Journal of Databases (OJDB)},
        issn      = {2199-3459},
        year      = {2014},
        volume    = {1},
        number    = {1},
        pages     = {1--2},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201705194557},
        urn       = {urn:nbn:de:101:1-201705194557},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {The Open Journal of Databases (OJDB) is a new open access journal covering all aspects of database research and technology. In this editorial, the first issue of the journal is presented.}
    }
    @Article{OJDB-v1i1n02_Duchateau,
        title     = {Designing a Benchmark for the Assessment of Schema Matching Tools},
        author    = {Fabien Duchateau and
                     Zohra Bellahsene},
        journal   = {Open Journal of Databases (OJDB)},
        issn      = {2199-3459},
        year      = {2014},
        volume    = {1},
        number    = {1},
        pages     = {3--25},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201705194573},
        urn       = {urn:nbn:de:101:1-201705194573},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Over the years, many schema matching approaches have been developed to discover correspondences between schemas. Although this task is crucial in data integration, its evaluation, both in terms of matching quality and time performance, is still manually performed. Indeed, there is no common platform which gathers a collection of schema matching datasets to fulfil this goal. Another problem deals with the measuring of the post-match effort, a human cost that schema matching approaches aim at reducing. Consequently, we propose XBenchMatch, a schema matching benchmark with available datasets and new measures to evaluate this manual post-match effort and the quality of integrated schemas. We finally report the results obtained by different approaches, namely COMA++, Similarity Flooding and YAM. We show that such a benchmark is required to understand the advantages and failures of schema matching approaches. Therefore, it could help an end-user to select a schema matching tool which covers his/her needs.}
    }
    @Article{OJDB-v1i1n03_Elbushra,
        title     = {Eventual Consistent Databases: State of the Art},
        author    = {Mawahib Musa Elbushra and
                     Jan Lindstr{\"o}m},
        journal   = {Open Journal of Databases (OJDB)},
        issn      = {2199-3459},
        year      = {2014},
        volume    = {1},
        number    = {1},
        pages     = {26--41},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201705194582},
        urn       = {urn:nbn:de:101:1-201705194582},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {One of the challenges of cloud programming is to achieve the right balance between the availability and consistency in a distributed database. Cloud computing environments, particularly cloud databases, are rapidly increasing in importance, acceptance and usage in major applications, which need the partition-tolerance and availability for scalability purposes, but sacrifice the consistency side (CAP theorem). In these environments, the data accessed by users is stored in a highly available storage system, thus the use of paradigms such as eventual consistency became more widespread. In this paper, we review the state-of-the-art database systems using eventual consistency from both industry and research. Based on this review, we discuss the advantages and disadvantages of eventual consistency, and identify the future research challenges on the databases using eventual consistency.}
    }