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  • 1
    Online Resource
    Online Resource
    PINOCCHIO: Probabilistic Influence-Based Location Selection over Moving Objects
    Institute of Electrical and Electronics Engineers (IEEE) ; 2016
    In:  IEEE Transactions on Knowledge and Data Engineering Vol. 28, No. 11 ( 2016-11-1), p. 3068-3082
    Details
    In: IEEE Transactions on Knowledge and Data Engineering, Institute of Electrical and Electronics Engineers (IEEE), Vol. 28, No. 11 ( 2016-11-1), p. 3068-3082
    Type of Medium: Online Resource
    ISSN: 1041-4347
    URL: Article
    DOI: 10.1109/TKDE.2016.2580138
    RVK:
    SA 5571
    Language: Unknown
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016
    detail.hit.zdb_id: 1001468-8
    detail.hit.zdb_id: 2026620-0
    SSG: 24,1
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  • 2
    Online Resource
    Online Resource
    AutoG : a visual query autocompletion framework for graph databases
    Association for Computing Machinery (ACM) ; 2016
    In:  Proceedings of the VLDB Endowment Vol. 9, No. 13 ( 2016-09), p. 1505-1508
    Details
    In: Proceedings of the VLDB Endowment, Association for Computing Machinery (ACM), Vol. 9, No. 13 ( 2016-09), p. 1505-1508
    Abstract: Composing queries is evidently a tedious task. This is particularly true of graph queries as they are typically complex and prone to errors, compounded by the fact that graph schemas can be missing or too loose to be helpful for query formulation. Despite the great success of query formulation aids, in particular, automatic query completion , graph query autocompletion has received much less research attention. In this demonstration, we present a novel interactive visual subgraph query autocompletion framework called A uto G which alleviates the potentially painstaking task of graph query formulation. Specifically, given a large collection of small or medium-sized graphs and a visual query fragment q formulated by a user, A uto G returns top- k query suggestions Q ′ as output at interactive time. Users may choose a query from Q ′ and iteratively apply A uto G to compose their queries. We demonstrate various features of A uto G and its superior ability to generate high quality suggestions to aid visual subgraph query formulation.
    Type of Medium: Online Resource
    ISSN: 2150-8097
    URL: Article
    DOI: 10.14778/3007263.3007295
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2016
    detail.hit.zdb_id: 2478691-3
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  • 3
    Online Resource
    Online Resource
    PANDA : a system for partial topology-based search on large networks
    Association for Computing Machinery (ACM) ; 2018
    In:  Proceedings of the VLDB Endowment Vol. 11, No. 12 ( 2018-08), p. 1966-1969
    Details
    In: Proceedings of the VLDB Endowment, Association for Computing Machinery (ACM), Vol. 11, No. 12 ( 2018-08), p. 1966-1969
    Abstract: A large body of research on subgraph query processing on large networks assumes that a query is posed in the form of a connected graph. Unfortunately, end users in practice may not always have precise knowledge about the topological relationships between nodes in a query graph to formulate a connected query. In this demonstration, we present a novel graph querying paradigm called partial topology-based network search and a query processing system called panda to efficiently find top-k matches of a partial topology query ( ptq ) in a single machine. A ptq is a disconnected query graph containing multiple connected query components . ptq s allow an end user to formulate queries without demanding precise information about the complete topology of a query graph. We demonstrate various innovative features of panda and its promising performance.
    Type of Medium: Online Resource
    ISSN: 2150-8097
    URL: Article
    DOI: 10.14778/3229863.3236236
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2018
    detail.hit.zdb_id: 2478691-3
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  • 4
    Online Resource
    Online Resource
    Towards plug-and-play visual graph query interfaces : data-driven selection of canned patterns for large networks
    Association for Computing Machinery (ACM) ; 2021
    In:  Proceedings of the VLDB Endowment Vol. 14, No. 11 ( 2021-07), p. 1979-1991
    Details
    In: Proceedings of the VLDB Endowment, Association for Computing Machinery (ACM), Vol. 14, No. 11 ( 2021-07), p. 1979-1991
    Abstract: Canned patterns ( i.e. , small subgraph patterns) in visual graph query interfaces (a.k.a GUI) facilitate efficient query formulation by enabling pattern-at-a-time construction mode. However, existing GUIS for querying large networks either do not expose any canned patterns or if they do then they are typically selected manually based on domain knowledge. Unfortunately, manual generation of canned patterns is not only labor intensive but may also lack diversity for supporting efficient visual formulation of a wide range of subgraph queries. In this paper, we present a novel, generic, and extensible framework called TATTOO that takes a data-driven approach to automatically select canned patterns for a GUI from large networks. Specifically, it first decomposes the underlying network into truss-infested and truss-oblivious regions. Then candidate canned patterns capturing different real-world query topologies are generated from these regions. Canned patterns based on a user-specified plug are then selected for the GUI from these candidates by maximizing coverage and diversity , and by minimizing the cognitive load of the pattern set. Experimental studies with real-world datasets demonstrate the benefits of TATTOO. Importantly, this work takes a concrete step towards realizing plug-and-play visual graph query interfaces for large networks.
    Type of Medium: Online Resource
    ISSN: 2150-8097
    URL: Article
    DOI: 10.14778/3476249.3476256
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2021
    detail.hit.zdb_id: 2478691-3
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  • 5
    Online Resource
    Online Resource
    MOCHA : a tool for visualizing impact of operator choices in query execution plans for database education
    Association for Computing Machinery (ACM) ; 2022
    In:  Proceedings of the VLDB Endowment Vol. 15, No. 12 ( 2022-08), p. 3602-3605
    Details
    In: Proceedings of the VLDB Endowment, Association for Computing Machinery (ACM), Vol. 15, No. 12 ( 2022-08), p. 3602-3605
    Abstract: The database systems course is offered in many major universities. A key learning goal of learners taking such a course is to understand how sql queries are processed in an RDBMS in practice. To this end, comprehension of the impact of various physical operators on the selected query execution plan (QEP) of a query is paramount. Unfortunately, off-the-shelf RDBMS typically only expose the QEP to users without revealing information about the impact of alternative choices of various physical operators on it in a user-friendly manner to aid learning. In this demonstration, we present a novel system called MOCHA that facilitates exploration and visualization of the impact of alternative physical operator choices on the QEP of a given SQL query. MOCHA accepts an SQL query as input, and compares and visualizes the QEP and alternative plans which are selected based on learner-specified operator preferences. Furthermore, it intuitively explains why the key operators in a QEP are chosen by connecting them to established knowledge in the literature.
    Type of Medium: Online Resource
    ISSN: 2150-8097
    URL: Article
    DOI: 10.14778/3554821.3554854
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2022
    detail.hit.zdb_id: 2478691-3
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  • 6
    Online Resource
    Online Resource
    Affinity-driven blog cascade analysis and prediction
    Springer Science and Business Media LLC ; 2014
    In:  Data Mining and Knowledge Discovery Vol. 28, No. 2 ( 2014-3), p. 442-474
    Details
    In: Data Mining and Knowledge Discovery, Springer Science and Business Media LLC, Vol. 28, No. 2 ( 2014-3), p. 442-474
    Type of Medium: Online Resource
    ISSN: 1384-5810 , 1573-756X
    URL: Article
    DOI: 10.1007/s10618-013-0307-0
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2014
    detail.hit.zdb_id: 1386325-3
    detail.hit.zdb_id: 1479890-6
    SSG: 24,1
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  • 7
    Online Resource
    Online Resource
    Authenticated Subgraph Similarity Searchin Outsourced Graph Databases
    Institute of Electrical and Electronics Engineers (IEEE) ; 2015
    In:  IEEE Transactions on Knowledge and Data Engineering Vol. 27, No. 7 ( 2015-7-1), p. 1838-1860
    Details
    In: IEEE Transactions on Knowledge and Data Engineering, Institute of Electrical and Electronics Engineers (IEEE), Vol. 27, No. 7 ( 2015-7-1), p. 1838-1860
    Type of Medium: Online Resource
    ISSN: 1041-4347
    URL: Article
    DOI: 10.1109/TKDE.2014.2316818
    RVK:
    SA 5571
    Language: Unknown
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2015
    detail.hit.zdb_id: 1001468-8
    detail.hit.zdb_id: 2026620-0
    SSG: 24,1
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  • 8
    Online Resource
    Online Resource
    Efficient Shapelet Discovery for Time Series Classification
    Institute of Electrical and Electronics Engineers (IEEE) ; 2022
    In:  IEEE Transactions on Knowledge and Data Engineering Vol. 34, No. 3 ( 2022-3-1), p. 1149-1163
    Details
    In: IEEE Transactions on Knowledge and Data Engineering, Institute of Electrical and Electronics Engineers (IEEE), Vol. 34, No. 3 ( 2022-3-1), p. 1149-1163
    Type of Medium: Online Resource
    ISSN: 1041-4347 , 1558-2191 , 2326-3865
    URL: Article
    DOI: 10.1109/TKDE.2020.2995870
    RVK:
    SA 5571
    Language: Unknown
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2022
    detail.hit.zdb_id: 1001468-8
    detail.hit.zdb_id: 2026620-0
    SSG: 24,1
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  • 9
    Online Resource
    Online Resource
    PICASSO : exploratory search of connected subgraph substructures in graph databases
    Association for Computing Machinery (ACM) ; 2017
    In:  Proceedings of the VLDB Endowment Vol. 10, No. 12 ( 2017-08), p. 1861-1864
    Details
    In: Proceedings of the VLDB Endowment, Association for Computing Machinery (ACM), Vol. 10, No. 12 ( 2017-08), p. 1861-1864
    Abstract: Recently, exploratory search has received much attention in information retrieval and database fields. This search paradigm assists users who do not have a clear search intent and are unfamiliar with the underlying data space. Specifically, query formulation evolves iteratively as the user becomes more familiar with the content. Despite its growing importance, exploratory search on graph-structured data has received little attention in the literature. We demonstrate a system called picasso to realize exploratory sub-structure search on a graph database containing a set of small or medium-sized data graphs. picasso embodies several novel features such as progressive ( i.e. , iterative) formulation of queries visually and incremental processing, multi-stream results exploration wall to visualize, explore, and analyze search results to identify possible search directions.
    Type of Medium: Online Resource
    ISSN: 2150-8097
    URL: Article
    DOI: 10.14778/3137765.3137794
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2017
    detail.hit.zdb_id: 2478691-3
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  • 10
    Online Resource
    Online Resource
    SENSOR : data-driven construction of sketch-based visual query interfaces for time series data
    Association for Computing Machinery (ACM) ; 2022
    In:  Proceedings of the VLDB Endowment Vol. 15, No. 12 ( 2022-08), p. 3650-3653
    Details
    In: Proceedings of the VLDB Endowment, Association for Computing Machinery (ACM), Vol. 15, No. 12 ( 2022-08), p. 3650-3653
    Abstract: Sketching is a common approach to visually query time series data. However, a recent study reported that sketching a pattern for querying is "often ineffective on its own" in practice due to lack of "representative objects" to facilitate bottom-up search. In this demonstration, we present a novel data-driven sketch-based visual query interface (VQI) construction system called SENSOR to alleviate this challenge. Given a time series dataset, SENSOR automatically constructs its VQI by populating different components from the underlying data. Specifically, it discovers and exposes a set of representative objects in the form of VST-aware shapelets to facilitate query formulation. Such data-driven construction has several potential benefits such as empowering efficient top-down and bottom-up search and portability of the interface across different application domains and sources.
    Type of Medium: Online Resource
    ISSN: 2150-8097
    URL: Article
    DOI: 10.14778/3554821.3554866
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2022
    detail.hit.zdb_id: 2478691-3
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