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David Taniar

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4 papers

2 author rows

SoCS Conference 2018 Conference Paper

Fast k-Nearest Neighbor on a Navigation Mesh

Shizhe Zhao
David Taniar
Daniel Harabor

We consider the k-Nearest Neighbour problem in a two-dimensional Euclidean plane with obstacles (OkNN). Existing and state of the art algorithms for OkNN are based on incremental visibility graphs and as such suffer from a well known disadvantage: costly and online visibility checking with quadratic worst-case running times. In this work we develop a new OkNN algorithm which avoids these disadvantages by representing the traversable space as a collection of convex polygons; i. e. a Navigation Mesh. We then adapt an recent and optimal navigation mesh algorithm, Polyanya, from the single-source single-target setting to the the multi-target case. We also give two new heuristics for OkNN. In a range of empirical comparisons we show that our approach can be orders of magnitude faster than competing methods that rely on visibility graphs.

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SoCS Conference 2018 Conference Paper

k -Nearest Neighbors on Road Networks: Euclidean Heuristic Revisited

Tenindra Abeywickrama
Muhammad Aamir Cheema
David Taniar

In the age of smartphones, finding the nearest points of interest (POIs) is a highly relevant problem. A popular way to solve this is to use a k Nearest Neighbor (kNN) query to retrieve POIs by their road network distances from a query location. However, we find that existing kNN methods have not been carefully compared. We present a detailed and fair experimental study of the state-of-the-art, documenting the many insights gleaned along the way. Notably, a long overlooked Euclidean distance heuristic is often the best performing method by a wide margin. We have also released all code as open-source for readers to reproduce experiments and easily add methods or queries to the testbed for new studies.

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KER Journal 2014 Journal Article

Time constraint route search over multi-locations

Geng Zhao
Kefeng Xuan
David Taniar
Maytham Safar
Bala Srinivasan

Abstract Traditional Route Search aims at finding the path that goes through geographical entities that are relevant to the provided search terms from the start point to the end point. Without constraints, traditional Route Search visiting multiple locations is unreliable because locations may close after a specified time. In this paper, time constraint (operating hours of each location) is drawn into Route Search query in order to make the query more realistic. Two methods are proposed in this paper, namely Route Search for fixed locations ( RFix ) and Route Search for flexible locations ( RFlex ). These two queries are different from the existing Route Search query because (1) the end point is not pre-defined and (2) time constraint is involved. Our two proposal queries consider whether the locations are specifically pre-defined by the user or only the location types are specified. In each method, two propositions are presented for pruning expansion branches, which highly improves the performance. Our experiments verified the applicability of RFix and RFlex to solve Route Search queries with time constraint queries.

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IS Journal 2007 Journal Article

Domain-Driven, Actionable Knowledge Discovery

Longbing Cao
Chengqi Zhang
Qiang Yang
David Bell
Michail Vlachos
Bahar Taneri
Eamonn Keogh
Philip S. Yu

Data mining increasingly faces complex challenges in the real-life world of business problems and needs. The gap between business expectations and R&D results in this area involves key aspects of the field, such as methodologies, targeted problems, pattern interestingness, and infrastructure support. Both researchers and practitioners are realizing the importance of domain knowledge to close this gap and develop actionable knowledge for real user needs.

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