Statistical structures for internet-scale data management

Efficient query processing in traditional database management systems relies on statistics on base data. For centralized systems, there is a rich body of research results on such statistics, from simple aggregates to more elaborate synopses such as sketches and histograms. For Internet-scale distributed systems, on the other hand, statistics management still poses major challenges. With the work in this paper we aim to endow peer-to-peer data management over structured overlays with the power associated with such statistical information, with emphasis on meeting the scalability challenge. To this end, we first contribute efficient, accurate, and decentralized algorithms that can compute key aggregates such as Count, CountDistinct, Sum, and Average. We show how to construct several types of histograms, such as simple Equi-Width, Average-Shifted Equi-Width, and Equi-Depth histograms. We present a full-fledged open-source implementation of these tools for distributed statistical synopses, and report on a comprehensive experimental performance evaluation, evaluating our contributions in terms of efficiency, accuracy, and scalability

Regional Data Archiving and Management for Northeast Illinois

This project studies the feasibility and implementation options for establishing a regional data archiving system to help monitor and manage traffic operations and planning for the northeastern Illinois region. It aims to provide a clear guidance to the regional transportation agencies, from both technical and business perspectives, about building such a comprehensive transportation information system. Several implementation alternatives are identified and analyzed. This research is carried out in three phases. In the first phase, existing documents related to ITS deployments in the broader Chicago area are summarized, and a thorough review is conducted of similar systems across the country. Various stakeholders are interviewed to collect information on all data elements that they store, including the format, system, and granularity. Their perception of a data archive system, such as potential benefits and costs, is also surveyed. In the second phase, a conceptual design of the database is developed. This conceptual design includes system architecture, functional modules, user interfaces, and examples of usage. In the last phase, the possible business models for the archive system to sustain itself are reviewed. We estimate initial capital and recurring operational/maintenance costs for the system based on realistic information on the hardware, software, labor, and resource requirements. We also identify possible revenue opportunities. A few implementation options for the archive system are summarized in this report; namely: 1. System hosted by a partnering agency 2. System contracted to a university 3. System contracted to a national laboratory 4. System outsourced to a service provider The costs, advantages and disadvantages for each of these recommended options are also provided.ICT-R27-22published or submitted for publicationis peer reviewe

Preliminary specification and design documentation for software components to achieve catallaxy in computational systems

This Report is about the preliminary specifications and design documentation for software components to achieve Catallaxy in computational systems. -- Die Arbeit beschreibt die Spezifikation und das Design von Softwarekomponenten, um das Konzept der Katallaxie in Grid Systemen umzusetzen. Eine Einführung ordnet das Konzept der Katallaxie in bestehende Grid Taxonomien ein und stellt grundlegende Komponenten vor. Anschließend werden diese Komponenten auf ihre Anwendbarkeit in bestehenden Application Layer Netzwerken untersucht.Grid Computing

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

How Far Removed Are You? Scalable Privacy-Preserving Estimation of Social Path Length with Social PaL

Social relationships are a natural basis on which humans make trust decisions. Online Social Networks (OSNs) are increasingly often used to let users base trust decisions on the existence and the strength of social relationships. While most OSNs allow users to discover the length of the social path to other users, they do so in a centralized way, thus requiring them to rely on the service provider and reveal their interest in each other. This paper presents Social PaL, a system supporting the privacy-preserving discovery of arbitrary-length social paths between any two social network users. We overcome the bootstrapping problem encountered in all related prior work, demonstrating that Social PaL allows its users to find all paths of length two and to discover a significant fraction of longer paths, even when only a small fraction of OSN users is in the Social PaL system - e.g., discovering 70% of all paths with only 40% of the users. We implement Social PaL using a scalable server-side architecture and a modular Android client library, allowing developers to seamlessly integrate it into their apps.Comment: A preliminary version of this paper appears in ACM WiSec 2015. This is the full versio