G. Bhalotia, A. Hulgeri, C. Nakhe, S. Chakrabarti, and S. Sudarshan. Keyword searching and browsing in databases using BANKS. ICDE, 2002. Home/Search Document Details and Download
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DB-Enabled Peers for Managing Distributed Data - Ooi, Shu, Tan (Correct)
....from a selected relation. 4.6 Joining Tuples based on Contents Query by keywords is the standard information retrieval mechanism to search for text documents. Recently, keyword search has also been proposed for searching centralized relational databases( e.g. DBXplore [1] Discover[9] and BANKS[2]) In general, it is technically very challenging to query a database using keywords due to the semantics of keywords. In PeerDB, an on going work is to provide keyword search in a peer based data management system setting. Unlike centralized systems, the key challenges are the autonomy of peers, ....
G. Bhalotia, C. Nakhe, A. Hulgeri, S. Chakrabarti, and S. Sudarshan. Keyword searching and browsing in databases using banks. In Proceedings of the 18th International Conference on Data Engineering, San Jose, CA, April 2002.
Efficient IR-Style Keyword Search over Relational.. - Hristidis, Gravano.. (2003) (1 citation) (Correct)
....of the system. Section 5 introduces the key query processing algorithms, which we evaluate experimentally in Section 6. Finally, Section 7 concludes the paper. 2 Related Work Recent research has addressed the problem of free form keyword search over structured and semi structured data. BANKS [2] views a database as a graph where the database tuples (or objects) are the nodes and application specific relationships are the edges. For example, an edge may denote a foreign key relationship. BANKS answers keyword queries by searching for Steiner trees [15] containing all keywords, using ....
....for a given query. Result ranking has been addressed by other keyword search systems for relational data. Given a query Q, both DISCOVER [11] and DBXplorer [1] assign a score to a joining tree of tuples T in the following way: size(T ) if T contains all words in Q Alternatively, BANKS [2] uses the following scoring scheme: # fr (T) fn (T) fp (T ) if T contains all words in Q where f r (T ) measures how related the relations of the tuples of T are, f n (T ) depends on the weight of the tuples of T as determined by a PageRank inspired technique , and f p (T ) is a ....
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G. Bhalotia, A. Hulgeri, C. Nakhey, S. Chakrabarti, and S. Sudarshan. Keyword searching and browsing in databases using BANKS. In ICDE, 2002.
Keyword Proximity Search on XML Graphs - Hristidis, Papakonstantinou, Balmin (2003) (9 citations) (Correct)
.... 2001] name [ John ] date [Nov 3 2001] person order lineitem nation [ US ] quantity [10 ] shipdate [Oct 14 2001] name [ Mike ] date [Oct 4 2001] order lineitem quantity [10 ] shipdate [Oct 15 2001] date [Oct 3 2001] part partkey [1005] name [ TV ] lineitem quantity [6] shipdate [Oct 14 2001] supplier linepart supplier linepart supplier linepart supplier linepart part partkey [1008] name [ VCR ] product prodkey [2005] descr [ set of VCR and DVD ] service call date [Nov 13 2001] descr [DVD error] part partkey [1009] name [ VCR ] ....
.... document p1: person [name= John nation= US ] quantity=10 [quantity=10 [partkey=1005 name= TV ] partkey=1008 [partkey=1009 Figure 2: Multivalued dependencies in results XKeyword follows a recent generation of information retrieval systems that provide keyword proximity search [14, 16, 6, 3] to structured and semistructured databases. In particular, XKeyword provides keyword proximity search on XML data that are modeled as labeled graphs, where the edges correspond to the element subelement relationship and to IDREF pointers. XKeyword di#ers from prior systems for proximity search on ....
[Article contains additional citation context not shown here]
G. Bhalotia, C. Nakhey, A. Hulgeri, S. Chakrabarti, and S. Sudarshanz. Keyword Searching and Browsing in Databases using BANKS. ICDE, 2002.
Mining the Space of Graph Properties - Jeh, Widom (Correct)
....as in the query of Figure 7. This feature was also exhibited in the proximity search of [5] which finds nodes in a graph that are nearby in terms of graph distance. However, there is no mechanism in [5] for explaining query results, one of the strengths of our approach. A system was presented in [4] that, given keywords matching tuples across different tables in a relational database, returns a tree denoting the schema relating the matching tuples, where the edges of the tree are foreign key relationships. The tree serves to explain how the tuples are related. However, these tree structures ....
Gaurav Bhalotia, Arvind Hulgeri, Charuta, Soumen Chakrabarti, and S. Sudarshan. Keyword searching and browsing in databases using BANKS. In Proceedings of the 18th International Conference on Data Engineering (ICDE), San Jose, California, USA, February 2002.
Is Question Answering an Acquired Skill? - Soumen (2004) Self-citation (Chakrabarti) (Correct)
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G. Bhalotia, A. Hulgeri, C. Nakhe, S. Chakrabarti, and S. Sudarshan. Keyword searching and browsing in databases using BANKS. In ICDE, San Jose, CA, 2002. IEEE.
Is Question Answering an Acquired Skill? - Ramakrishnan, Chakrabarti.. (2004) Self-citation (Chakrabarti) (Correct)
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G. Bhalotia, A. Hulgeri, C. Nakhe, S. Chakrabarti, and S. Sudarshan. Keyword searching and browsing in databases using BANKS. In ICDE, San Jose, CA, 2002. IEEE.
Introduccion a Los Sistemas De Bases De Datos - Ullman, Widom (1999) Self-citation (Bhalotia Hulgeri Nakhe Chakrabarti Sudarshan) (Correct)
....with a path to each leaf. We set the weight of (v, u)totheweightof(u, v) multiplied by a function of the number of links to v from the nodes of the same type as u. Experiments with di#erent functions indicated that the function log(1 x) where x is the number of inlinks, provided good results [3]. If there was already an edge from v to u,wesetthe edge weight to the lower of the original edge weight and the weight computed above. BANKS incorporates another interesting feature, namely node weights, inspired by prestige rankings such as PageRank in Google [4] With this feature, nodes ....
....score. We experimented with additive and multiplicative combinations, and found that both worked well when the relative weights for the two scores were appropriately chosen. Details of the search algorithm and the relevance computation, along with a preliminary performance study can be found in [3]. Although a few other systems implement keyword search on databases (e.g. 5, 1, 6] BANKS di#ers from all prior work in several ways: notably, in the techniques for edge weight computation and prestige based ranking, and the use of an in memory graph structure for very e#cient search while ....
[Article contains additional citation context not shown here]
Gaurav Bhalotia, Arvind Hulgeri, Charuta Nakhe, Soumen Chakrabarti, and S. Sudarshan. Keyword searching and browsing in databases using BANKS. In Procs. ICDE, Feb. 2002.
Keyword Searching and Browsing in Databases using BANKS - Bhalotia, Hulgeri.. (2002) (18 citations) Self-citation (Bhalotia Hulgeri Nakhe Chakrabarti Sudarshan) (Correct)
....avoid generating answers of low relevance that the user may never look at. In this section, we present an outline of the backward expanding search algorithm which offers a heuristic solution for incrementally computing query results. Complete details can be found in the full version of the paper [3]. We assume that the graph fits in memory. This is not unreasonable, even for moderately large databases, because the in memory node representation need not store any attribute of the corresponding tuple other than the RID. The only other in memory structure is an index to map RIDs to the graph ....
G. Bhalotia, A. Hulgeri, C. Nakhe, S. Chakrabarti, and S. Sudarshan. Keyword searching and browsing in databases using BANKS. Technical report, Indian Institute of Technology, Bombay, November 2001.
Circumventing Data Quality Problems - Using Multiple Join (Correct)
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G. Bhalotia, A. Hulgeri, C. Nakhe, S. Chakrabarti, and S. Sudarshan. Keyword searching and browsing in databases using BANKS. ICDE, 2002.
The SphereSearch Engine for Unified Ranked Retrieval of .. - Graupmann, Schenkel.. (2005) (Correct)
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G. Bhalotia et al. Keyword searching and browsing in databases using BANKS. In ICDE 2002.
Searching for Related Objects in Relational Databases - Yin, Han, Yang (Correct)
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G. Bhalotia, A. Hulgeri, C. Nakhe, S. Chakrabarti, S. Sudarshan. Keyword Searching and Browsing in Databases using BANKS. ICDE, 2002.
A System for Keyword Proximity Search on XML Databases - Balmin, Hristidis.. (2003) (Correct)
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G. Bhalotia, C. Nakhey, A. Hulgeri, S. Chakrabarti, and S. Sudarshan. Keyword searching and browsing in databases using BANKS. ICDE, 2002.
Relational Data Sharing in Peer-based Data Management Systems - Beng Chin Ooi (2003) (4 citations) (Correct)
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G. Bhalotia, C. Nakhe, A. Hulgeri, S. Chakrabarti, and S. Sudarshan. Keyword searching and browsing in databases using banks. In Proceedings of the 18th International Conference on Data Engineering, San Jose, CA, April 2002.
ObjectRank: Authority-Based Keyword Search in Databases - Balmin, Hristidis.. (2004) (13 citations) (Correct)
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G. Bhalotia, C. Nakhey, A. Hulgeri, S. Chakrabarti, and S. Sudarshan. Keyword Searching and Browsing in Databases using BANKS. ICDE, 2002.
Relational Link-Based Ranking - Floris Geerts Heikki (2004) (2 citations) (Correct)
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G. Bhalotia, A. Hulgeri, C. Nakhe, S. Chakrabarti, and S. Sudarshan. Keyword searching and browsing in databases using
Relational Link-Based Ranking - Geerts, Mannila, Terzi (2004) (2 citations) (Correct)
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G. Bhalotia, A. Hulgeri, C. Nakhe, S. Chakrabarti, and S. Sudarshan. Keyword searching and browsing in databases using
Translating Naive User Queries on the Semantic Web - Baoshi Yan Robert (2003) (Correct)
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G. Bhalotia, A. Hulgeri, C. Nakhe, and S. Chakrabarti. Keyword searching and browsing in databases using banks. In the 18th International Conference on Data Engineering (ICDE.02), 2002.
XRANK: Ranked Keyword Search over XML Documents - Lin Guo Feng (2003) (24 citations) (Correct)
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G. Bhalotia, et al., "Keyword Searching and Browsing in Databases using BANKS", ICDE Conf., 2002.
A System for Keyword Proximity Search on XML Databases - Balmin, Hristidis.. (2003) (Correct)
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G. Bhalotia, C. Nakhey, A. Hulgeri, S. Chakrabarti, and S. Sudarshan. Keyword searching and browsing in databases using BANKS. ICDE, 2002.
On the Integration of Structure Indexes and Inverted.. - Kaushik, Krishnamurthy, .. (2004) (5 citations) (Correct)
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G. Bhalotia et al. Keyword searching and browsing in databases using BANKS. In ICDE, 2002.
DISCOVER: Keyword Search in Relational Databases - Vagelis Hristidis University (2002) (14 citations) (Correct)
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G. Bhalotia, C. Nakhey, A. Hulgeri, S. Chakrabarti, and S. Sudarshanz. Keyword Searching and Browsing in Databases using BANKS. Proceedings of International Conference on Data Engineering, 2002.
DISCOVER: Keyword Search in Relational Databases - Hristidis, Papakonstantinou (2002) (14 citations) (Correct)
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G. Bhalotia, C. Nakhey, A. Hulgeri, S. Chakrabarti, and S. Sudarshanz. Keyword Searching and Browsing in Databases using BANKS. Proceedings of International Conference on Data Engineering, 2002.
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