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    @Article{OJBD_2017v3n01_Lehmann,
        title     = {Technology Selection for Big Data and Analytical Applications},
        author    = {Denis Lehmann and
                     David Fekete and
                     Gottfried Vossen},
        journal   = {Open Journal of Big Data (OJBD)},
        issn      = {2365-029X},
        year      = {2017},
        volume    = {3},
        number    = {1},
        pages     = {1--25},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201711266876},
        urn       = {urn:nbn:de:101:1-201711266876},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {The term Big Data has become pervasive in recent years, as smart phones, televisions, washing machines, refrigerators, smart meters, diverse sensors, eyeglasses, and even clothes connect to the Internet. However, their generated data is essentially worthless without appropriate data analytics that utilizes information retrieval, statistics, as well as various other techniques. As Big Data is commonly too big for a single person or institution to investigate, appropriate tools are being used that go way beyond a traditional data warehouse and that have been developed in recent years. Unfortunately, there is no single solution but a large variety of different tools, each of which with distinct functionalities, properties and characteristics. Especially small and medium-sized companies have a hard time to keep track, as this requires time, skills, money, and specific knowledge that, in combination, result in high entrance barriers for Big Data utilization. This paper aims to reduce these barriers by explaining and structuring different classes of technologies and the basic criteria for proper technology selection. It proposes a framework that guides especially small and mid-sized companies through a suitable selection process that can serve as a basis for further advances.}
    }