Mind your step! : How profiling location reveals your identity - and how you prepare for it

Location-based services (LBS) are services that position your mobile phone to provide some context-based service for you. Some of these services – called ‘location tracking’ applications - need frequent updates of the current position to decide whether a service should be initiated. Thus, internet-based systems will continuously collect and process the location in relationship to a personal context of an identified customer. This paper will present the concept of location as part of a person’s identity. I will conceptualize location in information systems and relate it to concepts like privacy, geographical information systems and surveillance. The talk will present how the knowledge of a person's private life and identity can be enhanced with data mining technologies on location profiles and movement patterns. Finally, some first concepts about protecting location information

Digital disease detection and participatory surveillance: overview and perspectives for Brazil

ABSTRACT This study aimed to describe the digital disease detection and participatory surveillance in different countries. The systems or platforms consolidated in the scientific field were analyzed by describing the strategy, type of data source, main objectives, and manner of interaction with users. Eleven systems or platforms, developed from 1996 to 2016, were analyzed. There was a higher frequency of data mining on the web and active crowdsourcing as well as a trend in the use of mobile applications. It is important to provoke debate in the academia and health services for the evolution of methods and insights into participatory surveillance in the digital age

Application of data mining to intensive care unit microbiologic data.

We describe refinements to and new experimental applications of the Data Mining Surveillance System (DMSS), which uses a large electronic health-care database for monitoring emerging infections and antimicrobial resistance. For example, information from DMSS can indicate potentially important shifts in infection and antimicrobial resistance patterns in the intensive care units of a single health-care facility

An agent-based intelligent environmental monitoring system

Fairly rapid environmental changes call for continuous surveillance and on-line decision making. There are two main areas where IT technologies can be valuable. In this paper we present a multi-agent system for monitoring and assessing air-quality attributes, which uses data coming from a meteorological station. A community of software agents is assigned to monitor and validate measurements coming from several sensors, to assess air-quality, and, finally, to fire alarms to appropriate recipients, when needed. Data mining techniques have been used for adding data-driven, customized intelligence into agents. The architecture of the developed system, its domain ontology, and typical agent interactions are presented. Finally, the deployment of a real-world test case is demonstrated.Comment: Multi-Agent Systems, Intelligent Applications, Data Mining, Inductive Agents, Air-Quality Monitorin

Incremental learning for Volcano monitoring

This document studies the creation of a computer program written in Java programming language to classify seismic movements occurred between 2015 and 2016 collected from volcano Puracé in Colombia, with the inclusion in this system of Data Mining application MOA by the University of Waikato. It is offered an initial study of aspects to take into consideration for the problem, presenting for a more profound analysis of the questions from a technological point of view, various suggestions and ways of confronting it. By performing a series of tests using surveillance data from volcano Puracé and applying modifications to these data, it’s derived the accuracy of Data Mining algorithms and necessary processes to produce a base structure for real-time applications related to seismic movement analysis. The analysis of the data is also performed, along with derivation of models of prediction using the program built for this project.Ingeniería Informática (Plan 2011

Spatial Data Mining Analytical Environment for Large Scale Geospatial Data

Nowadays, many applications are continuously generating large-scale geospatial data. Vehicle GPS tracking data, aerial surveillance drones, LiDAR (Light Detection and Ranging), world-wide spatial networks, and high resolution optical or Synthetic Aperture Radar imagery data all generate a huge amount of geospatial data. However, as data collection increases our ability to process this large-scale geospatial data in a flexible fashion is still limited. We propose a framework for processing and analyzing large-scale geospatial and environmental data using a “Big Data” infrastructure. Existing Big Data solutions do not include a specific mechanism to analyze large-scale geospatial data. In this work, we extend HBase with Spatial Index(R-Tree) and HDFS to support geospatial data and demonstrate its analytical use with some common geospatial data types and data mining technology provided by the R language. The resulting framework has a robust capability to analyze large-scale geospatial data using spatial data mining and making its outputs available to end users