The Criminal Law and Law Enforcement Implications of Big Data

2018 ◽  
Vol 14 (1) ◽  
pp. 293-308 ◽  
Author(s):  
Sarah Brayne
Keyword(s):  
Big Data ◽  
Law Enforcement ◽  
Data Collection ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Police Departments ◽  
Law Enforcement Agencies ◽  
Future Directions ◽  
Legal Questions ◽  
Legal Responses

Law enforcement agencies increasingly use big data analytics in their daily operations. This review outlines how police departments leverage big data and new surveillant technologies in patrol and investigations. It distinguishes between directed surveillance—which involves the surveillance of individuals and places under suspicion—and dragnet surveillance—which involves suspicionless, unparticularized data collection. Law enforcement's adoption of big data analytics far outpaces legal responses to the new surveillant landscape. Therefore, this review highlights open legal questions about data collection, suspicion requirements, and police discretion. It concludes by offering suggestions for future directions for researchers and practitioners.

Predict and Surveil

2020 ◽  
Author(s):  
Sarah Brayne
Keyword(s):  
Big Data ◽  
Law Enforcement ◽  
Los Angeles ◽  
Data Analytics ◽  
Predictive Analytics ◽  
Civil Liberties ◽  
Big Data Analytics ◽  
Police Department ◽  
Law Enforcement Agencies ◽  
Criminal Suspects

The scope of criminal justice surveillance, from policing to incarceration, has expanded rapidly in recent decades. At the same time, the use of big data has spread across a range of fields, including finance, politics, health, and marketing. While law enforcement’s use of big data is hotly contested, very little is known about how the police actually use it in daily operations and with what consequences. This book offers an inside look at how police use big data and new surveillance technologies, leveraging on-the-ground fieldwork with one of the most technologically advanced law enforcement agencies in the world—the Los Angeles Police Department. Drawing on original interviews and ethnographic observations from over two years of fieldwork with the LAPD, the text examines the causes and consequences of big data and algorithmic control. It reveals how the police use predictive analytics and new surveillance technologies to deploy resources, identify criminal suspects, and conduct investigations; how the adoption of big data analytics transforms police organizational practices; and how the police themselves respond to these new data-driven practices. While big data analytics has the potential to reduce bias, increase efficiency, and improve prediction accuracy, the book argues that it also reproduces and deepens existing patterns of inequality, threatens privacy, and challenges civil liberties.

RELATIONSHIP IN IOT AND BIG DATA ANALYTICS: THE OPPORTUNITIES AND OPEN CHALLENGES

2021 ◽  
Vol 83 (4) ◽  
pp. 100-111
Author(s):  
Ahmad Anwar Zainuddin ◽  
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Physical World ◽  
Future Directions ◽  
Wide Range ◽  
Time Utilization ◽  
Relationship Of ◽  
The Internet Of Things

Internet of Things (IoT) is an up-and-coming technology that has a wide variety of applications. It empowers physical objects to be organized in a specialized framework to grow its convenience in terms of ease and time utilization. It is to convert the thought of bridging the crevice between the physical world and the machine world. It is also being use in the wide range of the technology in this current situation. One of its applications is to monitor and store data over time from numerous devices allows for easy analysis of the dataset. This analysis can then be the basis of decisions made on the same. In this study, the concept, architecture, and relationship of IoT and Big Data are described. Next, several use cases in IoT and big data in the research methodology are studied. The opportunities and open challenges which including the future directions are described. Furthermore, by proposing a new architecture for big data analytics in the Internet of Things, this paper adds value. Overall, the various types of big IoT data analytics, their methods, and associated big data mining technologies are discussed.

scholarly journals SIDeKa: The Role of Information Technology for Knowledge Creation

2017 ◽  
Vol 2 (6) ◽  
pp. 570
Author(s):  
Cungki Kusdarjito
Keyword(s):  
Information Technology ◽  
Big Data ◽  
Data Collection ◽  
Knowledge Creation ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Unstructured Data ◽  
Village Level ◽  
The Village

The advancement of big data analytics is paving the way for knowledge creation based on very huge and unstructured data. Currently, information is scattered and growth tremendously, containing many information but difficult to be interpreted. Consequently, traditional approaches are no longer suitable for unstructured data but very rich in information. This situation is different from the role of previous information technology in which information is based on structured data, stored in the local storage, and in more advanced form, information can be retrieved through internet. Meanwhile, in Indonesia data are collected by many institutions with different measurement standard. The nature of the data collection is top-down, carried out by survey which is expensive yet unreliable and stored exclusively by respective institution. SIDeKa (Sistem Informasi Desa dan Kawasan/Village and Regional Information System), which are connected nationally, is proposed as a system of data collection in the village level and prepared by local people. Using SIDeKa, data reliability and readiness can be improved at the local level. The goals of the SIDeKa is not only local people have information in their hand such as poverty level, production, commodity price, the area of cultivated land, and the outbreak of diseases in their village, but also they have information from the neighboring villages or event at the national level. For government, data reliability will improve the policy effectiveness. This paper discusses the implementation and role of SIDeKa for knowledge creation in the village level, especially for the agricultural activities which has been initiated in 2015.Keywords: big data analytics; SIDeKa;  unstructured data.

Big-data analytics: a critical review and some future directions

2015 ◽  
Vol 10 (4) ◽  
pp. 337 ◽  
Author(s):  
Uroš Jovanovič ◽  
Aleš Štimec ◽  
Daniel Vladušič ◽  
Gregor Papa ◽  
Jurij Šilc
Keyword(s):  
Big Data ◽  
Critical Review ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Future Directions

Big data analytics – enabled cyber-physical system: model and applications

2019 ◽  
Vol 119 (5) ◽  
pp. 1072-1088
Author(s):  
Shuai Luo ◽  
Hongwei Liu ◽  
Ershi Qi
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Collection ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Cyber Physical System ◽  
Content Type ◽  
Aero Engine ◽  
Communication Layer ◽  
Smart Data

Purpose The purpose of this paper is to propose a comprehensive framework for integrating big data analytics (BDA) into cyber-physical system (CPS) solutions. This framework provides a wide range of functions, including data collection, smart data preprocessing, smart data mining and smart data visualization. Design/methodology/approach The architecture of CPS was designed with cyber layer, physical layer and communication layer from the perspective of big data processing. The BDA model was integrated into a CPS that enables managers to make sound decisions. Findings The effectiveness of the proposed BDA model has been demonstrated by two practical cases − the prediction of energy output of the power grid and the estimate of the remaining useful life of the aero-engine. The method can be used to control the power supply system and help engineers to maintain or replace the aero-engine to maintain the safety of the aircraft. Originality/value The communication layer, which connects the cyber layer and physical layer, was designed in CPS. From the communication layer, the redundant raw data can be converted into smart data. All the necessary functions of data collection, data preprocessing, data storage, data mining and data visualization can be effectively integrated into the BDA model for CPS applications. These findings show that the proposed BDA model in CPS can be used in different environments and applications.

Big data analytics sentiment: US-China reaction to data collection by business and government

2018 ◽  
Vol 130 ◽  
pp. 45-55 ◽  
Author(s):  
Ryan C. LaBrie ◽  
Gerhard H. Steinke ◽  
Xiangmin Li ◽  
Joseph A. Cazier
Keyword(s):  
Big Data ◽  
Data Collection ◽  
Data Analytics ◽  
Big Data Analytics

scholarly journals Big data analytics meets social media: A systematic review of techniques, open issues, and future directions

2020 ◽  
pp. 101517
Author(s):  
Sepideh Bazzaz Abkenar ◽  
Mostafa Haghi Kashani ◽  
Ebrahim Mahdipour ◽  
Seyed Mahdi Jameii
Keyword(s):  
Systematic Review ◽  
Social Media ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Future Directions ◽  
Open Issues

scholarly journals Big Data Approach as an Institutional Innovation to Tackle Hong Kong’s Illegal Subdivided Unit Problem

2018 ◽  
Vol 10 (8) ◽  
pp. 2709 ◽  
Author(s):  
Yung Yau ◽  
Wai Lau
Keyword(s):  
Big Data ◽  
Data Collection ◽  
Transaction Costs ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Institutional Innovation ◽  
The Government ◽  
The City ◽  
Structured Questionnaire

While applications of big data have been extensively studied, discussion is mostly made from the perspectives of computer science, Internet services, and informatics. Alternatively, this article takes the big data approach as an institutional innovation and uses the problem of illegal subdivided units (ISUs) in Hong Kong as a case study. High transaction costs incurred in identification of suspected ISUs and associated enforcement actions lead to a proliferation of ISUs in the city. We posit that the deployment of big data analytics can lower these transaction costs, enabling the government to tackle the problem of illegal accommodations. We propose a framework for big data collection, analysis, and feedback. As the findings of a structured questionnaire survey reveal, building professionals believed that the proposed framework could reduce transaction costs of ISU identification. Yet, concerns associated with the big data approach like privacy and predictive policing were also raised by the professionals.

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