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    @Article{OJDB_2019v6i1n03_Schiff,
        title     = {Ontology-Based Data Access to Big Data},
        author    = {Simon Schiff and
                     Ralf M{\"o}ller and
                     {\"O}zg{\"u}r L. {\"O}zcep},
        journal   = {Open Journal of Databases (OJDB)},
        issn      = {2199-3459},
        year      = {2019},
        volume    = {6},
        number    = {1},
        pages     = {21--32},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-2018122318334350985847},
        urn       = {urn:nbn:de:101:1-2018122318334350985847},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Recent approaches to ontology-based data access (OBDA) have extended the focus from relational database systems to other types of backends such as cluster frameworks in order to cope with the four Vs associated with big data: volume, veracity, variety and velocity (stream processing). The abstraction that an ontology provides is a benefit from the enduser point of view, but it represents a challenge for developers because high-level queries must be transformed into queries executable on the backend level. In this paper, we discuss and evaluate an OBDA system that uses STARQL (Streaming and Temporal ontology Access with a Reasoning-based Query Language), as a high-level query language to access data stored in a SPARK cluster framework. The development of the STARQL-SPARK engine show that there is a need to provide a homogeneous interface to access both static and temporal as well as streaming data because cluster frameworks usually lack such an interface. The experimental evaluation shows that building a scalable OBDA system that runs with SPARK is more than plug-and-play as one needs to know quite well the data formats and the data organisation in the cluster framework.}
    }
    @Article{OJDB_2020v7i1n01_Poensgen,
        title     = {Quasi-Convex Scoring Functions in Branch-and-Bound Ranked Search},
        author    = {Peter Poensgen and
                     Ralf M{\"o}ller},
        journal   = {Open Journal of Databases (OJDB)},
        issn      = {2199-3459},
        year      = {2020},
        volume    = {7},
        number    = {1},
        pages     = {1--11},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-2019092919333113374958},
        urn       = {urn:nbn:de:101:1-2019092919333113374958},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {For answering top-k queries in which attributes are aggregated to a scalar value for defining a ranking, usually the well-known branch-and-bound principle can be used for efficient query answering. Standard algorithms (e.g., Branch-and-Bound Ranked Search, BRS for short) require scoring functions to be monotone, such that a top-k ranking can be computed in sublinear time in the average case. If monotonicity cannot be guaranteed, efficient query answering algorithms are not known. To make branch-and-bound effective with descending or ascending rankings (maximum top-k or minimum top-k queries, respectively), BRS must be able to identify bounds for exploring search partitions, and only for monotonic ranking functions this is trivial. In this paper, we investigate the class of quasi-convex functions used for scoring objects, and we examine how bounds for exploring data partitions can correctly and efficiently be computed for quasi-convex functions in BRS for maximum top-k queries. Given that quasi-convex scoring functions can usefully be employed for ranking objects in a variety of applications, the mathematical findings presented in this paper are indeed significant for practical top-k query answering.}
    }
    @Article{OJDB_2020v7i1n02_Poensgen,
        title     = {Branch-and-Bound Ranked Search by Minimizing Parabolic Polynomials},
        author    = {Peter Poensgen and
                     Ralf M{\"o}ller},
        journal   = {Open Journal of Databases (OJDB)},
        issn      = {2199-3459},
        year      = {2020},
        volume    = {7},
        number    = {1},
        pages     = {12--20},
        url       = {https://www.ronpub.com/ojdb/OJDB_2020v7i1n02_Poensgen.html},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {The Branch-and-Bound Ranked Search algorithm (BRS) is an efficient method for answering top-k queries based on R-trees using multivariate scoring functions. To make BRS effective with ascending rankings, the algorithm must be able to identify lower bounds of the scoring functions for exploring search partitions. This paper presents BRS supporting parabolic polynomials. These functions are common to minimize combined scores over different attributes and cover a variety of applications. To the best of our knowledge the problem to develop an algorithm for computing lower bounds for the BRS method has not been well addressed yet.}
    }