Sum-Max Monotonic Ranked joins for evaluating Top-k Twig queries on weighted data graphs

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.