Efficient AIS Data Processing for Environmentally Safe Shipping

Reducing ship accidents at sea is important to all economic, environmental, and cultural sectors of Greece. Despite an increase in traffic and national monitoring, ships formulate routes according to their best judgment risking an accident. In this study we take a dataset spanning in 3 years from the AIS (Automatic Identification System) network, which is transmitting in public a ship's identity and location with an interval of seconds, and we load it in a trajectory database supported by the Hermes Moving Objects Database (MOD) system. Presented analysis begins by extracting statistics for the dataset, both general (number of ships and position reports) as well as safety related ones. Simple queries on the dataset illustrate the capabilities of Hermes and allow to gain insight on how the ships move in the Greek Seas. Analysis of movement based on an Origin-Destination matrix between interesting areas in the Greek territory is presented. One of the newest challenges that emerged during this process is that the amount of the positioning data is becoming more and more massive. As a conclusion, a preliminary review of possible solutions to this challenge along with others such as dealing with the noise in AIS data is mentioned and we also briefly discuss the need for interdisciplinary cooperation.This research was partially supported by AMINESS project funded by the Greek government (www.aminess.eu). Cyril Ray was supported by a Short Term Scientific Mission performed at the University of Piraeus by the COST Action IC0903 on “Knowledge Discovery from Moving Objects” (http://www.move-cost.info). IMIS Hellas (www.imishel las.gr) kindly provided the AIS dataset for research purposes

An artificial immune system for fuzzy-rule induction in data mining

This work proposes a classification-rule discovery algorithm integrating artificial immune systems and fuzzy systems. The algorithm consists of two parts: a sequential covering procedure and a rule evolution procedure. Each antibody (candidate solution) corresponds to a classification rule. The classification of new examples (antigens) considers not only the fitness of a fuzzy rule based on the entire training set, but also the affinity between the rule and the new example. This affinity must be greater than a threshold in order for the fuzzy rule to be activated, and it is proposed an adaptive procedure for computing this threshold for each rule. This paper reports results for the proposed algorithm in several data sets. Results are analyzed with respect to both predictive accuracy and rule set simplicity, and are compared with C4.5rules, a very popular data mining algorithm

Discovering Rules for Fault Management

. At the heart of the Internet revolution is global telecommunication systems. These systems initially designed for voice traffic provide the vast backbone bandwidth capabilities necessary for Internet traffic. They have builtin redundancy and complexity to ensure robustness and quality of service. To facilitate this, this requires complex fault identification and management systems. Fault identification and management is generally handled by reducing the amount of alarm events (symptoms) presented to the operating engineer through monitoring, filtering and masking. The ultimate goal is to determine and present the actual underlying fault. While en-route to automated fault identification it is useful to derive rules and techniques to attempt to present less symptoms with greater diagnostic assistance. With these objectives in mind computerassisted human discovery and human-assisted computer discovery techniques are discussed.