Event-Driven Surveillance: Possibilities and Challenges by Kerstin Denecke

By Kerstin Denecke

The net has turn into a wealthy resource of non-public details within the previous few years. humans twitter, weblog, and chat on-line. present emotions, studies or newest information are published. for example, first tricks to affliction outbreaks, patron personal tastes, or political alterations should be pointed out with this knowledge.

Surveillance or early caution structures permit such detection of adjustments and aid people in getting details on altering events. besides the fact that, the range of information which may be thought of for surveillance is massive, starting from sensor-measured values to amassed counts and data extracted from normal language documents.

Denecke’s target is to introduce the a number of chances and aspects of surveillance and its functions. She first introduces the duty of surveillance and offers an summary on surveillance in numerous domain names. subsequent, a number of the info assets which are to be had and will already be utilized by surveillance platforms are summarized. mostly a part of the booklet, her concentration is on unstructured facts as a resource for surveillance. an summary on current equipment in addition to how you can be constructed on the way to method this type of info with admire to surveillance is gifted. as an instance program, she introduces sickness surveillance utilizing net 2.0, together with corresponding equipment and demanding situations to be addressed. The publication closes with comments on new chances for surveillance received through contemporary advancements of the net and cellular verbal exchange, and with an overview of destiny challenges.

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3 Information extraction extracts relevant data from unstructured text (left) and fills it into a predefined template (right) based on regular expressions [64]. Patterns and fix extraction rules are useful when very specific information need to be extracted [35]. A detailed survey on information extraction methods is given by Chang et al. [12]. Text Analysis Tools For the various NLP tasks described before, tools are often already available. 2 lists some examples. They provide rich linguistic annotations.

The data analysis starts once data is acquired by the data collection and preprocessing methods presented before (see Fig. 4). From the structured data determined by natural language processing and information extraction methods, a feature set can be established. Then, a variety of analytical processes can be applied to this feature set to determine patterns, identify trends or generate signals. In this section, an overview on corresponding methods for event detection, data mining and signal generation is given.

The objective of feature representation is to represent a text in a way so that texts in the same category have similar representative values, while texts in every other different category have very different representative values. Features used for classification are terms, noun chunks, lexical information, structural features such as sentence length and position [52]. Beyond, part of speech, terms of specific word classes like nouns or verbs, their morphological stem, the syntactic structure, extracted entities etc.

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