A decision support system: Automated crime report analysis and classification for e-government

Ku, Chih Hao; Leroy, Gondy

doi:10.1016/j.giq.2014.08.003

Cited by 79 publications

(36 citation statements)

References 34 publications

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“…Most of these communications are saved or transformed into written text and then archived in a digital format, which has led to opportunities for automatic text analysis using NLP techniques to improve the effectiveness of law enforcement [98]. A decision support system that combines the use of NLP techniques, similarity measures, and classification approaches is proposed by Ku and Leroy [99] to automate and facilitate crime analysis. Filtering reports and identifying those that are related to the same or similar crimes can provide useful information to analyse crime trends, which allows for apprehending suspects and improving crime prevention.…”

Section: Crime Analysismentioning

confidence: 99%

Social big data: Recent achievements and new challenges

2016

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a b s t r a c tBig data has become an important issue for a large number of research areas such as data mining, machine learning, computational intelligence, information fusion, the semantic Web, and social networks. The rise of different big data frameworks such as Apache Hadoop and, more recently, Spark, for massive data processing based on the MapReduce paradigm has allowed for the efficient utilisation of data mining methods and machine learning algorithms in different domains. A number of libraries such as Mahout and SparkMLib have been designed to develop new efficient applications based on machine learning algorithms. The combination of big data technologies and traditional machine learning algorithms has generated new and interesting challenges in other areas as social media and social networks. These new challenges are focused mainly on problems such as data processing, data storage, data representation, and how data can be used for pattern mining, analysing user behaviours, and visualizing and tracking data, among others. In this paper, we present a revision of the new methodologies that is designed to allow for efficient data mining and information fusion from social media and of the new applications and frameworks that are currently appearing under the "umbrella" of the social networks, social media and big data paradigms. (D. Camacho). petabytes (and even exabytes) in size, and the massive sizes of these datasets extend beyond the ability of average database software tools to capture, store, manage, and analyse them effectively.The concept of big data has been defined through the 3V model, which was defined in 2001 by Laney [5] as: "high-volume, highvelocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making". More recently, in 2012, Gartner [6] updated the definition as follows: "Big data is high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization". Both definitions refer to the three basic features of big data: Volume, Variety, and Velocity. Other organisations, and big data practitioners (e.g., researchers, engineers, and so on), have extended this 3V model to a 4V model by including a new "V": Value [7]. This model can be even extended to 5Vs if the concepts of Veracity is incorporated into the big data definition.Summarising, this set of * V-models provides a straightforward and widely accepted definition related to what is (and what is not) a big-data-based problem, application, software, or framework. These concepts can be briefly described as follows [5,7]:• Volume: refers to large amounts of any kind of data from any different sources, including mobile digital data creation devices and digital devices. The benefit from gathering, processing, and analysing these large amounts of data generates a number http://dx.

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Section: Crime Analysismentioning

confidence: 99%

Social big data: Recent achievements and new challenges

2016

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“…For example, NER enables further inferences made by computer algorithms, predicting the probability of an outcome, or providing capabilities to other forms of decision support systems [28]. In the e-Government domain, Ku et al (2008) and Ku and Leroy (2014) developed a crime information extraction module to extract relevant entities (Act, Age, Body Part, Clothes, Drug, Electronic, Event, Face, Hair, Job, People, Physical condition, Physical feature, Property, Scene, Shoes, Time, Vehicle, Weapon) from crime reports in order to assist decision making by law enforcement officers [29,30]. The crime-information extraction model adopted both a rule-based method and a dictionary method, which performs well enough for actual use.…”

Section: Ner In E-government Studiesmentioning

confidence: 99%

Introducing textual analysis tools for policy informatics

Hagen

Harrison

Uzuner

et al. 2015

Proceedings of the 16th Annual International Conference on Digital Government Research

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Electronic petitioning (e-petitioning) provides a unique and promising channel through which people can directly express their policy preferences. E-petitions may be viewed as a natural laboratory for determining subjects of public interest, and thus can be used by policy analysts to understand social needs and constraints. In this paper, we introduce textual analysis tools (such as NER and topic modeling) and extract three types of novel variables (informativeness, named entities, and 21 topics) from We the People petition texts. The regression result shows that informativeness, named location, and several topics are significantly correlated with the log of the signature counts. These exploratory but promising results indicate that textual analysis tools can complement traditional statistical methods by providing descriptive measures that are helpful for making causal inferences from electronic petition data. These new tools, we believe, will facilitate policy analysis and policy informatics by enabling meaningful use of large volumes of online archives containing public expression regarding policy preferences.

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“…For example, [4,5] propose new algorithms to define the similarity between the documents for the filtering and authentication of criminal information on the web. [1] uses data extraction and clustering methods of text-mining techniques in identifying criminal documents in Arabic.…”

Section: A Brief Overview Of Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…These activities usually have various purposes, including propaganda against the state and dissemination of information, which promote terrorism and shake the foundations of national and spiritual values. [1][2][3][4][5][6][7].The timely detection of this information in e-government is very important in ensuring the security of the state and society, which is one of the most pressing scientific-theoretical and practical problems of our time [6,7]. It is no coincidence that the problem of e-government security is specified as one of the most actual 13 research directions to be studied in the eGovRTD2020 project adopted by the European Commission [8].…”

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confidence: 99%

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Detecting Terrorism-Related Articles on the E-Government Using Text-Mining Techniques

Alıguliyev¹,

Niftaliyeva²

2015

JPIT

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IntroductionModern criminal groups can commit malicious activities against the state and society not only in the real world, but also in a virtual environment (Internet, e-government). These activities usually have various purposes, including propaganda against the state and dissemination of information, which promote terrorism and shake the foundations of national and spiritual values. [1][2][3][4][5][6][7].The timely detection of this information in e-government is very important in ensuring the security of the state and society, which is one of the most pressing scientific-theoretical and practical problems of our time [6,7]. It is no coincidence that the problem of e-government security is specified as one of the most actual 13 research directions to be studied in the eGovRTD2020 project adopted by the European Commission [8].One of the main functions of e-government is to protect the citizens from possible impairment and violence. Linders [9] examined the evolution of citizen-state relations and concluded that the Internet, in particular e-government, is the most effective and affordable tool to inform about possible crimes, as well as to improve the relations between community members and law enforcement authorities. However, studies show that criminal groups "benefit" from this favorable environment as well, and this has become a source of great danger for the state and society. 9/11 is the best example. Further analysis has shown that the organized criminal group behind 9/11 planned and coordinated the entire act via the Internet. One can naturally conclude the virtual world is a very favorable environment for criminal groups to commit their evil deeds.Understandably, one of the important tasks of the modern state is to identify and analyze secret criminal networks operating via the Internet and e-state. This virtual space has a wide range of opportunities to communicate and coordinate operatively. Criminal group members can communicate via websites, e-mail, blogs, online chats, etc. In most cases, transmitted information consists of text. Therefore, analysis of the text transmitted through the virtual environment is essential to preventing possible terrorist acts and ensuring the security of e-government [10].Currently, text mining, the intelligent analysis of text gathered from various sources, is considered one of the most advanced and effective technologies in knowledge control [11].Another reason for the popularity of text mining and its wide range of applications is the dominance of text in the information, regardless of the information's sources. According to International Data Corporation analysts, about 80% of produced information contains text [12].

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A decision support system: Automated crime report analysis and classification for e-government

Cited by 79 publications

References 34 publications

Social big data: Recent achievements and new challenges

Social big data: Recent achievements and new challenges

Introducing textual analysis tools for policy informatics

Detecting Terrorism-Related Articles on the E-Government Using Text-Mining Techniques

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