Michael Shekelyan

• PhD
• Lecturer (Assistant Professor) at Queen Mary University of London

A bicriteria network is an interlinked data set where edges are labeled with two cost attributes. An example is a road network where edges represent road segments being labeled with traversal time and energy consumption. To measure the proximity of two nodes in network data, the common method is to compute a cost optimal path between the nodes. In...

Computing cost-optimal paths in network data is an important task in many application areas like transportation networks, computer networks, or social graphs. In many cases, the cost of an edge can be described by various cost criteria. For example, in a road network possible cost criteria are distance, time, ascent, energy consumption or toll fees...

In many graph applications, computing cost-optimal paths between two locations is an important task for routing and distance computation. Depending on the network multiple cost criteria might be of interest. Examples are travel time, energy consumption and toll fees in road networks. Path skyline queries compute the set of pareto optimal paths betw...

Joining records with all other records that meet a linkage condition can result in an astronomically large number of combinations due to many-to-many relationships. For such challenging (acyclic) joins, a random sample over the join result is a practical alternative to working with the oversized join result. Whereas prior works are limited to unifo...

Prefix sums are a powerful technique to answer range-sum queries over multi-dimensional arrays in O(1) time by looking up a constant number of values in an array of size O(N), where N is the number of cells in the multi-dimensional array. However, the technique suffers from O(N) update and storage costs. Relative prefix sums address the high update...

The prefix sum approach is a powerful technique to answer range-sum queries over multi-dimensional arrays in constant time by requiring only a few look-ups in an array of precomputed prefix sums. In this paper, we propose the sparse prefix sum approach that is based on relative prefix sums and exploits sparsity in the data to vastly reduce the stor...

We propose DigitHist, a histogram summary for selectivity estimation on multi-dimensional data with tight error bounds. By combining multi-dimensional and one-dimensional histograms along regular grids of different resolutions, DigitHist provides an accurate and reliable histogram approach for multi-dimensional data. To achieve a compact summary, w...

Full paper: http://www.dbs.ifi.lmu.de/Publikationen/Papers/ICDE15-SheJosSch.pdf Poster: http://bigdata.snu.ac.kr/icde2015/media/posters/426.pdf

Computing cost optimal paths in network data is a very important task in many application areas like transportation networks, computer networks or social graphs. In many cases, the cost of an edge can be described by various cost criteria. For example, in a road network possible cost criteria are distance, time, ascent, energy consumption or toll f...

Searching for similar image regions in medical databases yields valuable information for diagnosis. However, most of the current approaches are restricted to special cases or they are only available for rather small data stores. In this paper, we propose a fast query pipeline for 3D similarity queries on large databases of computed tomography (CT)...