% 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/
% Volume 2, Issue 1, 2015
    @Article{OJDB-2015v2i1n01_Buchmann,
        title     = {Deriving Bounds on the Size of Spatial Areas},
        author    = {Erik Buchmann and
                     Patrick Erik Bradley and
                     Klemens B{\"o}hm},
        journal   = {Open Journal of Databases (OJDB)},
        issn      = {2199-3459},
        year      = {2015},
        volume    = {2},
        number    = {1},
        pages     = {1--16},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201705194566},
        urn       = {urn:nbn:de:101:1-201705194566},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Many application domains such as surveillance, environmental monitoring or sensor-data processing need upper and lower bounds on areas that are covered by a certain feature. For example, a smart-city infrastructure might need bounds on the size of an area polluted with fine-dust, to re-route combustion-engine traffic. Obtaining such bounds is challenging, because in almost any real-world application, information about the region of interest is incomplete, e.g., the database of sensor data contains only a limited number of samples. Existing approaches cannot provide upper and lower bounds or depend on restrictive assumptions, e.g., the area must be convex. Our approach in turn is based on the natural assumption that it is possible to specify a minimal diameter for the feature in question. Given this assumption, we formally derive bounds on the area size, and we provide algorithms that compute these bounds from a database of sensor data, based on geometrical considerations. We evaluate our algorithms both with a real-world case study and with synthetic data.}
    }
    @Article{OJDB_2015v2i1n02_Elbushra,
        title     = {Causal Consistent Databases},
        author    = {Mawahib Musa Elbushra and
                     Jan Lindstr{\"o}m},
        journal   = {Open Journal of Databases (OJDB)},
        issn      = {2199-3459},
        year      = {2015},
        volume    = {2},
        number    = {1},
        pages     = {17--35},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201705194619},
        urn       = {urn:nbn:de:101:1-201705194619},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Many consistency criteria have been considered in databases and the causal consistency is one of them. The causal consistency model has gained much attention in recent years because it provides ordering of relative operations. The causal consistency requires that all writes, which are potentially causally related, must be seen in the same order by all processes. The causal consistency is a weaker criteria than the sequential consistency, because there exists an execution, which is causally consistent but not sequentially consistent, however all executions satisfying the sequential consistency are also causally consistent. Furthermore, the causal consistency supports non-blocking operations; i.e. processes may complete read or write operations without waiting for global computation. Therefore, the causal consistency overcomes the primary limit of stronger criteria: communication latency. Additionally, several application semantics are precisely captured by the causal consistency, e.g. collaborative tools. In this paper, we review the state-of-the-art of causal consistent databases, discuss the features, functionalities and applications of the causal consistency model, and systematically compare it with other consistency models. We also discuss the implementation of causal consistency databases and identify limitations of the causal consistency model.}
    }
    @Article{OJDB_2015v2i1n03_Groppe,
        title     = {PatTrieSort - External String Sorting based on Patricia Tries},
        author    = {Sven Groppe and
                     Dennis Heinrich and
                     Stefan Werner and
                     Christopher Blochwitz and
                     Thilo Pionteck},
        journal   = {Open Journal of Databases (OJDB)},
        issn      = {2199-3459},
        year      = {2015},
        volume    = {2},
        number    = {1},
        pages     = {36--50},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201705194627},
        urn       = {urn:nbn:de:101:1-201705194627},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {External merge sort belongs to the most efficient and widely used algorithms to sort big data: As much data as fits inside is sorted in main memory and afterwards swapped to external storage as so called initial run. After sorting all the data in this way block-wise, the initial runs are merged in a merging phase in order to retrieve the final sorted run containing the completely sorted original data. Patricia tries are one of the most space-efficient ways to store strings especially those with common prefixes. Hence, we propose to use patricia tries for initial run generation in an external merge sort variant, such that initial runs can become large compared to traditional external merge sort using the same main memory size. Furthermore, we store the initial runs as patricia tries instead of lists of sorted strings. As we will show in this paper, patricia tries can be efficiently merged having a superior performance in comparison to merging runs of sorted strings. We complete our discussion with a complexity analysis as well as a comprehensive performance evaluation, where our new approach outperforms traditional external merge sort by a factor of 4 for sorting over 4 billion strings of real world data.}
    }