Onion Curve: A Space Filling Curve with Near-Optimal Clustering

Space filling curves (SFCs) are widely used in the design of indexes for spatial and temporal data. Clustering is a key metric for an SFC, that measures how well the curve preserves locality in moving from higher dimensions to a single dimension. We present the {\em onion curve}, an SFC whose clustering performance is provably close to optimal for the cube and near-cube shaped query sets, irrespective of the side length of the query. We show that in contrast, the clustering performance of the widely used Hilbert curve can be far from optimal, even for cube-shaped queries. Since the clustering performance of an SFC is critical to the efficiency of multi-dimensional indexes based on the SFC, the onion curve can deliver improved performance for data structures involving multi-dimensional data.Comment: The short version is published in ICDE 1

Reducing Latency by Clustering Based Index Services using Hybrid Cache in Ad Hoc Networks

An efficient index structure is presented to guide mobile clients to the - objects. The proposed broadcast scheduling and indexing is aimed at minimizing query access time and energy consumption of the clients when retrieving - objects through wireless channels . W e design and evaluate cooperative caching techniques to efficiently support data access in ad hoc networks. We first propose two schemes: Cache Data , which caches the data, and Cache Path , which caches the data path. After analyzing the performance of those two schemes, we propose a hybrid approach ( Hybrid Cache ), which can further improve the performance by taking advantage of Cache Data and Cache Path while avoiding their weaknesses. Cache replacement policies are also studied to further improve the performance. Simulation results show that the proposed schemes can signifi cantly reduce the query delay and message complexity when compared to other caching schemes

Recursive tilings and space-filling curves with little fragmentation

This paper defines the Arrwwid number of a recursive tiling (or space-filling curve) as the smallest number w such that any ball Q can be covered by w tiles (or curve sections) with total volume O(vol(Q)). Recursive tilings and space-filling curves with low Arrwwid numbers can be applied to optimise disk, memory or server access patterns when processing sets of points in d-dimensional space. This paper presents recursive tilings and space-filling curves with optimal Arrwwid numbers. For d >= 3, we see that regular cube tilings and space-filling curves cannot have optimal Arrwwid number, and we see how to construct alternatives with better Arrwwid numbers.Comment: Manuscript accompanying abstract in EuroCG 2010, including full proofs, 20 figures, references, discussion et

Binary visualisation for malware detection

It is becoming increasingly harder to protect devices against security threats; as malware is steadily evolving defence mechanisms are struggling to persevere. This study introduces a concept intended at supporting security systems using Self-Organizing Incremental Neural Network (SOINN) and binary visualization. The system converts a file to its visual representation and sends the data for classification to SOINN. Tests were done to evaluate its performance and obtain an accuracy rate, which rounds the 80% figures at the moment, and false positive and negative rates. Bytes prevalence were also analysed with malware samples having a higher amount of null bytes compared with software samples, which may be a result of hiding malicious data or functionality. The patterns created by the samples were examined; malware samples had more clustering and created different patterns across the images whereas software samples presented mostly static and constant images although exceptions were noted in both categories