Abstracting Query Building for Multi-entity Faceted Browsing

被引:0
|
作者
Palm, Fredrik [1 ]
机构
[1] Umea Univ, HUMlab, S-90187 Umea, Sweden
关键词
Dynamic query building; faceted browsing; multidimensional exploration;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper presents an overview of work based on the QVIZ-project to support faceted browsing, focusing on the handling of larger, more complex relational database structures, discrete and continuous data, hierarchies, temporal and spatial data. Faceted browsing allows the creation of unpredictable arrangements of search criteria by the user. Such dynamics require a generic and abstracted mechanism in order to be able to adapt to multidimensional exploration and user requirements. Faceted browsers function through the progressive narrowing of choices in selected dimensions. This paper describes an approach using a graph representation of data models and shortest path operations to build queries. The system described is fully functional and has developed since 2007 at HUMlab, Umea University, Sweden. It is now being used in several digital humanities and multidisciplinary projects with different database schemata.
引用
收藏
页码:53 / 63
页数:11
相关论文
共 50 条
  • [41] Automated Comparative Table Generation for Facilitating Human Intervention in Multi-Entity Resolution
    Huang, Jiacheng
    Hu, Wei
    Li, Haoxuan
    Qu, Yuzhong
    ACM/SIGIR PROCEEDINGS 2018, 2018, : 585 - 594
  • [42] Joint Extraction Model of Multi-entity Relations for Poultry Diagnosis and Treatment Text
    Hu B.
    Tang B.
    Jiang H.
    Huo A.
    Han W.
    Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery, 2021, 52 (06): : 268 - 276
  • [43] The Multi-Entity Decision Graph Decision Ontology: A Decision Ontology for Fusion Support
    Locher, Mark
    Costa, Paulo C. G.
    2017 20TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION), 2017, : 1831 - 1838
  • [44] Reasoning with conceptual graphs and evidential networks for multi-entity maritime threat assessment
    Kowalski, Pawel
    Jousselme, Anne-Laure
    2022 25TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION 2022), 2022,
  • [46] Data-organization before learning Multi-Entity Bayesian Networks structure
    Bouhamed, H.
    Rebai, A.
    Lecroq, T.
    Jaoua, M.
    World Academy of Science, Engineering and Technology, 2011, 78 : 305 - 308
  • [47] Multi-Entity Bayesian Networks for Knowledge-Driven Analysis of ICH Content
    Chantas, Giannis
    Kitsikidis, Alexandros
    Nikolopoulos, Spiros
    Dimitropoulos, Kosmas
    Douka, Stella
    Kompatsiaris, Ioannis
    Grammalidis, Nikos
    COMPUTER VISION - ECCV 2014 WORKSHOPS, PT II, 2015, 8926 : 355 - 369
  • [48] Service Restoration in Multi-Entity Network- Cloud Ecosystems: How to Cooperate?
    Sahoo, Subhadeep
    Xu, Sugang
    Ferdousi, Sifat
    Hirota, Yusuke
    Tornatore, Massimo
    Awaji, Yoshinari
    Mukherjee, Biswanath
    IEEE COMMUNICATIONS MAGAZINE, 2025, 63 (03) : 129 - 135
  • [49] Game Theoretic Fuzzy Multi-Entity Bayesian Networks for Collision Avoidance in VANETs
    Golestan, Keyvan
    Soua, Ridha
    Karray, Fakhri
    Kamel, Mohamed S.
    2016 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE), 2016, : 508 - 515
  • [50] Predictive Situation Awareness Reference Model using Multi-Entity Bayesian Networks
    Park, Cheol Young
    Laskey, Kathryn Blackmond
    Costa, Paulo C. G.
    Matsumoto, Shou
    2014 17TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION), 2014,