TY - GEN
T1 - Sum-max monotonic ranked joins for evaluating top-k twig queries on weighted data graphs
AU - Qi, Yan
AU - Candan, Kasim
AU - Sapino, Maria Luisa
N1 - Publisher Copyright:
Copyright 2007 VLDB Endowment, ACM.
PY - 2007
Y1 - 2007
N2 - In many applications, the underlying data (the web, an XML document, or a relational database) can be seen as a graph. These graphs may be enriched with weights, associated with the nodes and edges of the graph, denoting application specific desirability/penalty assessments, such as popularity, trust, or cost. A particular challenge when considering such weights in query processing is that results need to be ranked accordingly. Answering keyword-based queries on weighted graphs is shown to be computationally expensive. In this paper, we first show that answering queries with further structure imposed on them remains NP-hard. We next show that, while the query evaluation task can be viewed in terms of ranked structural-joins along query axes, the monotonicity property, necessary for ranked join algorithms, is violated. Consequently, traditional ranked join algorithms are not directly applicable. Thus, we establish an alternative, sum-max monotonicity property and show how to leverage this for developing a self-punctuating, horizon-based ranked join (HR-Join) operator for ranked twig-query execution on data graphs. We experimentally show the effectiveness of the proposed evaluation schemes and the HR-join operator for merging ranked sub-results under sum-max monotonicity.
AB - In many applications, the underlying data (the web, an XML document, or a relational database) can be seen as a graph. These graphs may be enriched with weights, associated with the nodes and edges of the graph, denoting application specific desirability/penalty assessments, such as popularity, trust, or cost. A particular challenge when considering such weights in query processing is that results need to be ranked accordingly. Answering keyword-based queries on weighted graphs is shown to be computationally expensive. In this paper, we first show that answering queries with further structure imposed on them remains NP-hard. We next show that, while the query evaluation task can be viewed in terms of ranked structural-joins along query axes, the monotonicity property, necessary for ranked join algorithms, is violated. Consequently, traditional ranked join algorithms are not directly applicable. Thus, we establish an alternative, sum-max monotonicity property and show how to leverage this for developing a self-punctuating, horizon-based ranked join (HR-Join) operator for ranked twig-query execution on data graphs. We experimentally show the effectiveness of the proposed evaluation schemes and the HR-join operator for merging ranked sub-results under sum-max monotonicity.
UR - http://www.scopus.com/inward/record.url?scp=84864221851&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:84864221851
T3 - 33rd International Conference on Very Large Data Bases, VLDB 2007 - Conference Proceedings
SP - 507
EP - 518
BT - 33rd International Conference on Very Large Data Bases, VLDB 2007 - Conference Proceedings
A2 - Gehrke, Johannes
A2 - Koch, Christoph
A2 - Garofalakis, Minos
A2 - Aberer, Karl
A2 - Kanne, Carl-Christian
A2 - Neuhold, Erich J.
A2 - Ganti, Venkatesh
A2 - Klas, Wolfgang
A2 - Chan, Chee-Yong
A2 - Srivastava, Divesh
A2 - Florescu, Dana
A2 - Deshpande, Anand
PB - Association for Computing Machinery, Inc
T2 - 33rd International Conference on Very Large Data Bases, VLDB 2007
Y2 - 23 September 2007 through 27 September 2007
ER -