View Selection in Semantic Web Databases

We consider the setting of a Semantic Web database, containing both explicit data encoded in RDF triples, and implicit data, implied by the RDF semantics. Based on a query workload, we address the problem of selecting a set of views to be materialized in the database, minimizing a combination of query processing, view storage, and view maintenance costs. Starting from an existing relational view selection method, we devise new algorithms for recommending view sets, and show that they scale significantly beyond the existing relational ones when adapted to the RDF context. To account for implicit triples in query answers, we propose a novel RDF query reformulation algorithm and an innovative way of incorporating it into view selection in order to avoid a combinatorial explosion in the complexity of the selection process. The interest of our techniques is demonstrated through a set of experiments.Comment: VLDB201

Energy-Efficient Multi-View Video Transmission with View Synthesis-Enabled Multicast

Multi-view videos (MVVs) provide immersive viewing experience, at the cost of heavy load to wireless networks. Except for further improving viewing experience, view synthesis can create multicast opportunities for efficient transmission of MVVs in multiuser wireless networks, which has not been recognized in existing literature. In this paper, we would like to exploit view synthesis-enabled multicast opportunities for energy-efficient MVV transmission in a multiuser wireless network. Specifically, we first establish a mathematical model to characterize the impact of view synthesis on multicast opportunities and energy consumption. Then, we consider the optimization of view selection, transmission time and power allocation to minimize the weighted sum energy consumption for view transmission and synthesis, which is a challenging mixed discrete-continuous optimization problem. We propose an algorithm to obtain an optimal solution with reduced computational complexity by exploiting optimality properties. To further reduce computational complexity, we also propose two low-complexity algorithms to obtain two suboptimal solutions, based on continuous relaxation and Difference of Convex (DC) programming, respectively. Finally, numerical results demonstrate the advantage of the proposed solutions.Comment: 22 pages, 6 figures, to be published in GLOBECOM 201

An Integer Linear Programming Model for View Selection on Overlapping Camera Clusters

Multi-View Stereo (MVS) algorithms scale poorly on large image sets, and quickly become unfeasible to run on a single machine with limited memory. Typical solutions to lower the complexity include reducing the redundancy of the image set (view selection), and dividing the image set in groups to be processed independently (view clustering). A novel formulation for view selection is proposed here. We express the problem with an Integer Linear Programming (ILP) model, where cameras are modeled with binary variables, while the linear constraints enforce the completeness of the 3D reconstruction. The solution of the ILP leads to an optimal subset of selected cameras. As a second contribution, we integrate ILP camera selection with a view clustering approach which exploits Leveraged Affinity Propagation (LAP). LAP clustering can efficiently deal with large camera sets. We adapt the original algorithm so that it provides a set of overlapping clusters where the minimum and maximum sizes and the number of overlapping cameras can be specified. Evaluations on four different dataset show our solution provides significant complexity reductions and guarantees near-perfect coverage, making large reconstructions feasible even on a single machine

Phase transitions in Pareto optimal complex networks

The organization of interactions in complex systems can be described by networks connecting different units. These graphs are useful representations of the local and global complexity of the underlying systems. The origin of their topological structure can be diverse, resulting from different mechanisms including multiplicative processes and optimization. In spatial networks or in graphs where cost constraints are at work, as it occurs in a plethora of situations from power grids to the wiring of neurons in the brain, optimization plays an important part in shaping their organization. In this paper we study network designs resulting from a Pareto optimization process, where different simultaneous constraints are the targets of selection. We analyze three variations on a problem finding phase transitions of different kinds. Distinct phases are associated to different arrangements of the connections; but the need of drastic topological changes does not determine the presence, nor the nature of the phase transitions encountered. Instead, the functions under optimization do play a determinant role. This reinforces the view that phase transitions do not arise from intrinsic properties of a system alone, but from the interplay of that system with its external constraints.Comment: 14 pages, 7 figure

Greedy Selection of Materialized Views

Greedy based approach for view selection at each step selects a beneficial view that fits within the space available for view materialization. Most of these approaches are focused around the HRU algorithm, which uses a multidimensional lattice framework to determine a good set of views to materialize. The HRU algorithm exhibits high run time complexity as the number of possible views is exponential with respect to the number of dimensions. The PGA algorithm provides a scalable solution to this problem by selecting views for materialization in polynomial time relative to the number of dimensions. This paper compares the HRU and the PGA algorithm. It was experimentally deduced that the PGA algorithm, in comparison with the HRU algorithm, achieves an improved execution time with lowered memory and CPU usages. The HRU algorithm has an edge over the PGA algorithm on the quality of the views selected for materialization

Evaluation of Performance Based Appraisal System in Higher Education Sector using DEA and AHP

There is a broad interest in the study of schemes for the measurement of the efficiency of the higher education sector, which generates demand but at the same time is controversial because of the complexity of the problem. Performance evaluation in Higher Education institutions is one of the essential activities in teaching and learning procedure. This problem is associated with the highly combinatorial characteristics that occur when facing the selection of the proper combination of the attributes, namely inputs and outputs. This study proposes an integrated approach to measure performance based appraisal system (PBAS) in higher educational institutions combining Analytic Hierarchy Process (AHP) with Data Envelopment Analysis (DEA).The AHP allows consideration of the varying importance of each criterion of teaching performance, while DEA enables to the comparison of teachers on teaching as perceived by students with a view to identifying the scope for improvement by each teacher