Aplikasi Web Crawler Untuk Web Content Pada Mobile Phone

Crawling is the process behind a search engine, which served through the World Wide Web in a structured and with certain ethics. Applications that run the crawling process is called Web Crawler, also called web spider or web robot. The growth of mobile search services provider, followed by growth of a web crawler that can browse web pages in mobile content type. Crawler Web applications can be accessed by mobile devices and only web pages that type Mobile Content to be explored is the Web Crawler. Web Crawler duty is to collect a number of Mobile Content. A mobile application functions as a search application that will use the results from the Web Crawler. Crawler Web server consists of the Servlet, Mobile Content Filter and datastore. Servlet is a gateway connection between the client with the server. Datastore is the storage media crawling results. Mobile Content Filter selects a web page, only the appropriate web pages for mobile devices or with mobile content that will be forwarded

NEMESYS: Enhanced Network Security for Seamless Service Provisioning in the Smart Mobile Ecosystem

As a consequence of the growing popularity of smart mobile devices, mobile malware is clearly on the rise, with attackers targeting valuable user information and exploiting vulnerabilities of the mobile ecosystems. With the emergence of large-scale mobile botnets, smartphones can also be used to launch attacks on mobile networks. The NEMESYS project will develop novel security technologies for seamless service provisioning in the smart mobile ecosystem, and improve mobile network security through better understanding of the threat landscape. NEMESYS will gather and analyze information about the nature of cyber-attacks targeting mobile users and the mobile network so that appropriate counter-measures can be taken. We will develop a data collection infrastructure that incorporates virtualized mobile honeypots and a honeyclient, to gather, detect and provide early warning of mobile attacks and better understand the modus operandi of cyber-criminals that target mobile devices. By correlating the extracted information with the known patterns of attacks from wireline networks, we will reveal and identify trends in the way that cyber-criminals launch attacks against mobile devices.Comment: Accepted for publication in Proceedings of the 28th International Symposium on Computer and Information Sciences (ISCIS'13); 9 pages; 1 figur

Medical Analysis Question and Answering Application for Internet Enabled Mobile Devices

Mobile devices such as smart phones, the iPhone, and the iPad have become more popular in recent years. With access to the Internet through cellular or WIFI networks, these mobile devices can make use of the great source of information available on the Internet. Unlike a desktop or laptop computer, an Internet enabled mobile device is designed to be carried around and available to the owner almost instantly at any moment of the day. Despite having such great advantage and potential, searching for information with a mobile device remains a difficult task. Mobile device users have to juggle between different search engines through the web browser to seek useful information. The small screen of most mobile devices makes it very difficult to interact with the search engines using the device\u27s web browser. This project focuses on the development of a mobile application that attempts to make information searching easier for mobile device users. In addition, the application is designed to answer search queries that are related to the medical field. Search query in the medical field requires more selective results and that the results come from trusted 4 sources before it can be used. The application addresses this problem by introducing a database of community knowledge designed to store credible medically related articles that could help answer medical specific search queries. This would help minimize the need to look for answers from the Internet, which often could contain unreliable information. In addition to answering search queries, the application also allows user to store personal medical records and conveniently share that information as needed using email

REDUCING DISTRIBUTED URLS CRAWLING TIME : A COMPARISON OF GUIDS AND IDS

Web crawler visits websites for the purpose of indexing. The dynamic nature of today’s web makes the crawling process harder than before as web contents are continuously updated. In addition, crawling speed is important considering tsunami of big data that need to be indexed among competitive search engines. This research project is aimed to provide survey of current problems in distributed web crawlers. It then investigate the best crawling speed between dynamic globally unique identifiers (GUIDs) and the traditional static identifiers (IDs). Experiment are done by implementing Arachnot.net web crawlers to index up to 20000 locally generated URLs using both techniques. The results shown that URLs crawling time can be reduced up to 7% by using GUIDs technique instead of using IDs

A Survey of Volunteered Open Geo-Knowledge Bases in the Semantic Web

Over the past decade, rapid advances in web technologies, coupled with innovative models of spatial data collection and consumption, have generated a robust growth in geo-referenced information, resulting in spatial information overload. Increasing 'geographic intelligence' in traditional text-based information retrieval has become a prominent approach to respond to this issue and to fulfill users' spatial information needs. Numerous efforts in the Semantic Geospatial Web, Volunteered Geographic Information (VGI), and the Linking Open Data initiative have converged in a constellation of open knowledge bases, freely available online. In this article, we survey these open knowledge bases, focusing on their geospatial dimension. Particular attention is devoted to the crucial issue of the quality of geo-knowledge bases, as well as of crowdsourced data. A new knowledge base, the OpenStreetMap Semantic Network, is outlined as our contribution to this area. Research directions in information integration and Geographic Information Retrieval (GIR) are then reviewed, with a critical discussion of their current limitations and future prospects

Big Data Computing for Geospatial Applications

The convergence of big data and geospatial computing has brought forth challenges and opportunities to Geographic Information Science with regard to geospatial data management, processing, analysis, modeling, and visualization. This book highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates opportunities for using big data for geospatial applications. Crucial to the advancements highlighted in this book is the integration of computational thinking and spatial thinking and the transformation of abstract ideas and models to concrete data structures and algorithms

A social media and crowd-sourcing data mining system for crime prevention during and post-crisis situations

A number of large crisis situations, such as natural disasters have affected the planet over the last decade. The outcomes of such disasters are catastrophic for the infrastructures of modern societies. Furthermore, after large disasters, societies come face-to-face with important issues, such as the loss of human lives, people who are missing and the increment of the criminality rate. In many occasions, they seem unprepared to face such issues. This paper aims to present an automated system for the synchronization of the police and Law Enforcement Agencies (LEAs) for the prevention of criminal activities during and post a large crisis situation. The paper presents a review of the literature focusing on the necessity of using data mining in combination with advanced web technologies, such as social media and crowd-sourcing, for the resolution of the problems related to criminal activities caused during and post-crisis situations. The paper provides an introduction to examples of different techniques and algorithms used for social media and crowd-sourcing scanning, such as sentiment analysis and link analysis. The main focus of the paper is the ATHENA Crisis Management system. The function of the ATHENA system is based on the use of social media and crowd-sourcing for collecting crisis-related information. The system uses a number of data mining techniques to collect and analyze data from the social media for the purpose of crime prevention. A number of conclusions are drawn on the significance of social media and crowd-sourcing data mining techniques for the resolution of problems related to large crisis situations with emphasis to the ATHENA system