Web based mapping

Web mapping is the process of designing, implementing, generating and delivering maps on the World Wide Web and its product. It primarily deals with technological issues, while web cartographyadditionally studies theoretic aspects which include but not limited to the use of web maps, the evaluation and optimization of techniques and workflows, the usability of web maps, and social aspects. Web GIS on the other hand emphasises on analysis, processing of project specific geodataand exploratory aspects. Often the terms web GIS and web mapping are used synonymously, even if they don't mean exactly the same. Web maps are often a presentation media in web GIS and web maps are increasingly gaining analytical capabilities.

Mobile maps, displayed on mobile computing devices, such as mobile phones, smart phones, PDAs and GPS are a special case of web maps; they are regarded as mobile web maps if they are displayed by a mobile web browser or web user agent.

The use of web maps can be regarded as a major new trend in cartography and has opened up new opportunities like real-time maps, cheaper dissemination, more frequent and cheaper updates of data and software, personalized map content, distributed data sources and sharing of geographic information. With web mapping, freely available mapping technologies and geodata potentially allow every skilled person to produce web maps. The cheap and easy transfer of geodata across the internet allows the integration of distributed data sources, opening opportunities that go beyond the possibilities of disjoint data storage. Everyone with minimal knowhow and infrastructure can become a geodata provider; this puts geodata in the hands of untrained people who potentially violate cartographic and geographic principles and introduce flaws during the preparation, analysis and presentation of geographic and cartographic data.

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Fraud

Fraud is a million dollar business and it is increasing every year. Fraud involves one or more persons who intentionally act secretly to deprive another of something of value, for their own benefit. Fraud is as old as humanity itself and can take an unlimited variety of different forms. However, in recent years, the development of new technologies has also provided further ways in which criminals may commit fraud. In addition, business reengineering, reorganization or downsizing may weaken or eliminate control, while new information systems may present additional opportunities to commit fraud.

Traditional ways of data analysis have been in use since a long time as a method of detecting fraud. They require complex and time-consuming investigations that deal with different domains of knowledge like financial, economics, business practices and law. Fraud often consists of many instances or incidents involving repeated transgressions using the same method. Fraud instances can be similar in content and appearance but usually are not identical.

In the technological systems, fraudulent activities have occurred in many areas of daily life such as telecommunication networks, mobile communications, on-line banking, and Ecommerce. Fraud is increasing dramatically with the expansion of modern technology and global communication, resulting in substantial losses to the businesses. Consequentially, fraud detection has become an important issue to be explored.

Fraud detection involves identifying fraud as quickly as possible once it has been perpetrated. Fraud detection methods are continuously developed to defend criminals in adapting to their strategies. The development of new fraud detection methods is made more difficult due to the severe limitation of the exchange of ideas in fraud detection. Data sets are not made available and results are often not disclosed to the public. The fraud cases have to he detected from the available huge data sets such as the logged data and user behaviour. At present, fraud detection has been implemented by a number of methods such as data mining, statistics, and artificial intelligence. Fraud is discovered from anomalies in data and patterns. The objective of fraud detection is to maximize correct predictions and maintain incorrect predictions at an acceptable level.

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