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  • 1
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2023
    In:  Machine Learning
    In: Machine Learning, Springer Science and Business Media LLC
    Abstract: Automated fraud detection can assist organisations to safeguard user accounts, a task that is very challenging due to the great sparsity of known fraud transactions. Many approaches in the literature focus on credit card fraud and ignore the growing field of online banking. However, there is a lack of publicly available data for both. The lack of publicly available data hinders the progress of the field and limits the investigation of potential solutions. With this work, we: (a) introduce FraudNLP , the first anonymised, publicly available dataset for online fraud detection, (b) benchmark machine and deep learning methods with multiple evaluation measures, (c) argue that online actions do follow rules similar to natural language and hence can be approached successfully by natural language processing methods.
    Type of Medium: Online Resource
    ISSN: 0885-6125 , 1573-0565
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 1475529-4
    detail.hit.zdb_id: 54638-0
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  • 2
    Online Resource
    Online Resource
    Inderscience Publishers ; 2017
    In:  International Journal of Big Data Intelligence Vol. 4, No. 1 ( 2017), p. 3-
    In: International Journal of Big Data Intelligence, Inderscience Publishers, Vol. 4, No. 1 ( 2017), p. 3-
    Type of Medium: Online Resource
    ISSN: 2053-1389 , 2053-1397
    Language: English
    Publisher: Inderscience Publishers
    Publication Date: 2017
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  • 3
    Online Resource
    Online Resource
    Institute of Electrical and Electronics Engineers (IEEE) ; 2021
    In:  IEEE Transactions on Knowledge and Data Engineering Vol. 33, No. 6 ( 2021-6-1), p. 2392-2411
    In: IEEE Transactions on Knowledge and Data Engineering, Institute of Electrical and Electronics Engineers (IEEE), Vol. 33, No. 6 ( 2021-6-1), p. 2392-2411
    Type of Medium: Online Resource
    ISSN: 1041-4347 , 1558-2191 , 2326-3865
    RVK:
    Language: Unknown
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2021
    detail.hit.zdb_id: 1001468-8
    detail.hit.zdb_id: 2026620-0
    SSG: 24,1
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  • 4
    Online Resource
    Online Resource
    Association for Computing Machinery (ACM) ; 1998
    In:  ACM SIGMOD Record Vol. 27, No. 2 ( 1998-06), p. 564-566
    In: ACM SIGMOD Record, Association for Computing Machinery (ACM), Vol. 27, No. 2 ( 1998-06), p. 564-566
    Type of Medium: Online Resource
    ISSN: 0163-5808
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 1998
    detail.hit.zdb_id: 243829-X
    detail.hit.zdb_id: 2051432-3
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  • 5
    Online Resource
    Online Resource
    Association for Computing Machinery (ACM) ; 1999
    In:  ACM SIGMOD Record Vol. 28, No. 2 ( 1999-06), p. 455-466
    In: ACM SIGMOD Record, Association for Computing Machinery (ACM), Vol. 28, No. 2 ( 1999-06), p. 455-466
    Abstract: We address the problem of query rewriting for TSL, a language for querying semistructured data. We develop and present an algorithm that, given a semistructured query q and a set of semistructured views V , finds rewriting queries, i.e., queries that access the views and produce the same result as q . Our algorithm is based on appropriately generalizing containment mappings , the chase , and query composition — techniques that were developed for structured, relational data. We also develop an algorithm for equivalence checking of TSL queries. We show that the algorithm is sound and complete for TSL, i.e., it always finds every non-trivial TSL rewriting query of q , and we discuss its complexity. We extend the rewriting algorithm to use some forms of structural constraints (such as DTDs) and find more opportunities for query rewriting.
    Type of Medium: Online Resource
    ISSN: 0163-5808
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 1999
    detail.hit.zdb_id: 243829-X
    detail.hit.zdb_id: 2051432-3
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  • 6
    Online Resource
    Online Resource
    World Scientific Pub Co Pte Ltd ; 2017
    In:  International Journal on Artificial Intelligence Tools Vol. 26, No. 05 ( 2017-10), p. 1760024-
    In: International Journal on Artificial Intelligence Tools, World Scientific Pub Co Pte Ltd, Vol. 26, No. 05 ( 2017-10), p. 1760024-
    Abstract: Computing a (Union of Conjunctive Queries — UCQ) rewriting ℛ for an input query and ontology and evaluating it over the given dataset is a prominent approach to query answering over ontologies. However, ℛ can be large and complex in structure hence additional techniques, like query subsumption and data constraints, need to be employed in order to minimize ℛ and lead to an efficient evaluation. Although sound in theory, how to efficiently and effectively implement many of these techniques in practice could be challenging. For example, many systems do not implement query subsumption. In the current paper we present several practical techniques for UCQ rewriting minimization. First, we present an optimized algorithm for eliminating redundant (w.r.t. subsumption) queries as well as a novel framework for rewriting minimization using data constraints. Second, we show how these techniques can also be used to speed up the computation of ℛ in first place. Third, we integrated all our techniques in our query rewriting system IQAROS and conducted an extensive experimental evaluation using many artificial as well as challenging real-world ontologies obtaining encouraging results as, in the vast majority of cases, our system is more efficient compared to the two most popular state-of-the-art systems.
    Type of Medium: Online Resource
    ISSN: 0218-2130 , 1793-6349
    Language: English
    Publisher: World Scientific Pub Co Pte Ltd
    Publication Date: 2017
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  • 7
    Online Resource
    Online Resource
    Association for Computing Machinery (ACM) ; 2005
    In:  ACM Transactions on Internet Technology Vol. 5, No. 3 ( 2005-08), p. 570-570
    In: ACM Transactions on Internet Technology, Association for Computing Machinery (ACM), Vol. 5, No. 3 ( 2005-08), p. 570-570
    Type of Medium: Online Resource
    ISSN: 1533-5399 , 1557-6051
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2005
    detail.hit.zdb_id: 2060058-6
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  • 8
    Online Resource
    Online Resource
    Elsevier BV ; 2013
    In:  Information Systems Vol. 38, No. 8 ( 2013-11), p. 1285-1308
    In: Information Systems, Elsevier BV, Vol. 38, No. 8 ( 2013-11), p. 1285-1308
    Type of Medium: Online Resource
    ISSN: 0306-4379
    RVK:
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2013
    detail.hit.zdb_id: 194994-9
    detail.hit.zdb_id: 2012447-8
    SSG: 24,1
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  • 9
    Online Resource
    Online Resource
    Elsevier BV ; 2000
    In:  The Journal of Logic Programming Vol. 43, No. 1 ( 2000-04), p. 75-122
    In: The Journal of Logic Programming, Elsevier BV, Vol. 43, No. 1 ( 2000-04), p. 75-122
    Type of Medium: Online Resource
    ISSN: 0743-1066
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2000
    detail.hit.zdb_id: 1466385-5
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  • 10
    Online Resource
    Online Resource
    Association for Computing Machinery (ACM) ; 1997
    In:  ACM SIGMOD Record Vol. 26, No. 2 ( 1997-06), p. 532-535
    In: ACM SIGMOD Record, Association for Computing Machinery (ACM), Vol. 26, No. 2 ( 1997-06), p. 532-535
    Abstract: In order to access information from a variety of heterogeneous information sources, one has to be able to translate queries and data from one data model into another. This functionality is provided by so-called (source) wrappers [4,8] which convert queries into one or more commands/queries understandable by the underlying source and transform the native results into a format understood by the application. As part of the TSIMMIS project [1, 6] we have developed hard-coded wrappers for a variety of sources (e.g., Sybase DBMS, WWW pages, etc.) including legacy systems (Folio). However, anyone who has built a wrapper before can attest that a lot of effort goes into developing and writing such a wrapper. In situations where it is important or desirable to gain access to new sources quickly, this is a major drawback. Furthermore, we have also observed that only a relatively small part of the code deals with the specific access details of the source. The rest of the code is either common among wrappers or implements query and data transformation that could be expressed in a high level, declarative fashion. Based on these observations, we have developed a wrapper implementation toolkit [7] for quickly building wrappers. The toolkit contains a library for commonly used functions, such as for receiving queries from the application and packaging results. It also contains a facility for translating queries into source-specific commands, and for translating results into a model useful to the application. The philosophy behind our “template-based” translation methodology is as follows. The wrapper implementor specifies a set of templates (rules) written in a high level declarative language that describe the queries accepted by the wrapper as well as the objects that it returns. If an application query matches a template, an implementor-provided action associated with the template is executed to provide the native query for the underlying source 1 . When the source returns the result of the query, the wrapper transforms the answer which is represented in the data model of the source into a representation that is used by the application. Using this toolkit one can quickly design a simple wrapper with a few templates that cover some of the desired functionality, probably the one that is most urgently needed. However, templates can be added gradually as more functionality is required later on. Another important use of wrappers is in extending the query capabilities of a source. For instance, some sources may not be capable of answering queries that have multiple predicates. In such cases, it is necessary to pose a native query to such a source using only predicates that the source is capable of handling. The rest of the predicates are automatically separated from the user query and form a filter query . When the wrapper receives the results, a post-processing engine applies the filter query. This engine supports a set of built-in predicates based on the comparison operators =,≠, 〈 , 〉 , etc. In addition, the engine supports more complex predicates that can be specified as part of the filter query. The postprocessing engine is common to wrappers of all sources and is part of the wrapper toolkit. Note that because of postprocessing, the wrapper can handle a much larger class of queries than those that exactly match the templates it has been given. Figure 1 shows an overview of the wrapper architecture as it is currently implemented in our TSIMMIS testbed. Shaded components are provided by the toolkit, the white component is source-specific and must be generated by the implementor. The driver component controls the translation process and invokes the following services: the parser which parses the templates, the native schema, as well as the incoming queries into internal data structures, the matcher which matches a query against the set of templates and creates a filter query for postprocessing if necessary, the native component which submits the generated action string to the source, and extracts the data from the native result using the information given in the source schema, and the engine , which transforms and packages the result and applies a postprocessing filter if one has been created by the matcher. We now describe the sequence of events that occur at the wrapper during the translation of a query and its result using an example from our prototype system. The queries are formulated using a rule-based language called MSL that has been developed as a template specification and query language for the TSIMMIS project. Data is represented using our Object Exchange Model (OEM). We will briefly describe MSL and OEM in the next section. Details on MSL can be found in [5], a full introduction to OEM is given in [1] .
    Type of Medium: Online Resource
    ISSN: 0163-5808
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 1997
    detail.hit.zdb_id: 243829-X
    detail.hit.zdb_id: 2051432-3
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