Massive Data Collection Research Articles

Heterogeneous Differential Privacy

The massive collection of personal data by personalization systems has rendered the preservation of privacy of individuals more and more difficult. Most of the proposed approaches to preserve privacy in personalization systems usually address this issue uniformly across users, thus ignoring the fact that users have different privacy attitudes and expectations (even among their own personal data). In this paper, we propose to account for this non-uniformity of privacy expectations by introducing the concept of heterogeneous differential privacy. This notion captures both the variation of privacy expectations among users as well as across different pieces of information related to the same user. We also describe an explicit mechanism achieving heterogeneous differential privacy, which is a modification of the Laplacian mechanism by Dwork, McSherry, Nissim and Smith. In a nutshell, this mechanism achieves heterogeneous differential privacy by manipulating the sensitivity of the function using a linear transformation on the input domain. Finally, we evaluate on real datasets the impact of the proposed mechanism with respect to a semantic clustering task. The results of our experiments demonstrate that heterogeneous differential privacy can account for different privacy attitudes while sustaining a good level of utility as measured by the recall for the semantic clustering task.

Is Uncle Sam Stalking You? Abandoning Warrantless Electronic Surveillance to Preclude Intrusive Government Searches

The Founders drafted the Fourth Amendment foreseeing immense governmental abuse likely to stem from intrusive, unlimited searches. The Fourth Amendment bars any government investigative efforts absent a warrant grounded in a showing of probable cause. Further the Fourth Amendment specifically protects the “papers” of U.S. citizens. This is interpreted to extend to all private communication whether conducted through traditional or electronic channels.This article argues the Fourth Amendment mandates oversight in all surveillance conducted by the intelligence community. Administrations have made continuous efforts to conduct massive collections of electronic data — including private communications of U.S. citizens — without obtaining a warrant. The foreign surveillance exception was recognized to provide the Executive with an exception to the Fourth Amendment’s warrant requirement where the investigation was centered on national security. The numerous abuses stemming from the recognition of an exception, led Congress to draft the Federal Intelligence Surveillance Act. This statute mandated judiciary and legislative oversight of the Executive’s previously unchecked power to conduct intelligence surveillance.This article is the first to consider completely abandoning the foreign intelligence exception, to maintain all national security electronic surveillance with the oversight of the Federal International Surveillance Court.The Federal International Surveillance Court, organized through FISA, provides a tribunal for an independent judiciary to consider the constitutionality of the intelligence community’s electronic surveillance procedures. The court scrutinizes the particular intelligence purpose; the reasonableness of the surveillance; and maintains minimization procedures. This serves to promote efficient intelligence while preserving the protected privacy rights of Americans and promoting efficient intelligence.

Finding meaningful participation in volunteer geographic information and citizen science: a case comparison in environmental application

ABSTRACTVolunteer geographic information (VGI) and citizen science (CS) have been used as a way to promote a form of democratization of knowledge. Crowdsourcing facilitates the massive collection of data through nonexpert volunteers. This study compares 57 VGI and CS projects through a cross-case comparison method to explore the level of involvement of participants, based on three main analytic dimensions (project rationale, actor interaction, and data flow) and 12 sub-variables. The primary goal of this research is to analyze the purpose and the role of technology in VGI and CS projects, using three matrices to explore emerging trade-offs. Finally, we synthesized the workflow of VGI and CS project and compared based on project purposes. At the end of this study, five main purposes were found among the selected projects, uncovering that data collection is the most common purpose. A general workflow can be conceptualized, but detail implementation will involve various trade-offs, especially between local involvement and large-area expert-oriented protocol implementations. By exploring diverse relationships among selected variables of analysis in VGI and CS projects, this study pretends to build a foundation for larger, global meta-analysis that can bring some transparency in the epistemic interactions between experts and nonexperts.

The Alternative Epistemologies of Data Activism

Abstract As datafication progressively invades all spheres of contemporary society, citizens grow increasingly aware of the critical role of information as the new fabric of social life. This awareness triggers new forms of civic engagement and political action that we term “data activism”. Data activism indicates the range of sociotechnical practices that interrogate the fundamental paradigm shift brought about by datafication. Combining Science and Technology Studies with Social Movement Studies, this theoretical article offers a foretaste of a research agenda on data activism. It foregrounds democratic agency vis-à-vis datafication, and unites under the same label ways of affirmative engagement with data (“proactive data activism”, e. g. databased advocacy) and tactics of resistance to massive data collection (“reactive data activism”, e. g. encryption practices), understood as a continuum along which activists position and reposition themselves and their tactics. The article argues that data activism supports the emergence of novel epistemic cultures within the realm of civil society, making sense of data as a way of knowing the world and turning it into a point of intervention and generation of data countercultures. It offers the notion of data activism as a heuristic tool for the study of new forms of political participation and civil engagement in the age of datafication, and explores data activism as an evolving theoretical construct susceptible to contestation and revision.

City digital pulse: a cloud based heterogeneous data analysis platform

In recent years, increasing attention has been paid to developing exceptional technologies for efficiently processing massive collection of data. This is essential in the research on smart city, which involves various types of data generated by different kinds of sensors (hard and soft). In this paper, we propose a cloud-based platform named City Digital Pulse (CDP), where a unified mechanism and extensible architecture are provided to facilitate the various aspects in big data analysis, ranging from data acquisition to data visualization. We instantiate the proposed system using multi-model data collected from two social networks, namely Twitter and Instagram, which can provide instant geo-tagged data. Data analysis is performed to detect human affections from user uploaded content. The information revealed from the collected social data can be visualized at multiple dimensions through a well-designed Web application. This allows users to easily sense changes in human affective status and identify the underlying reasons. This offers priceless opportunities to improve the decision making in many critical tasks using the detected attitudes in the social messages, such as promotion strategy for companies or new policy making for the government. Our experiment results confirm the effectiveness of the proposed architecture and algorithms.

Reliable Collection of Real-Time Patient Physiologic Data from less Reliable Networks: a "Monitor of Monitors" System (MoMs).

Research and practice based on automated electronic patient monitoring and data collection systems is significantly limited by system down time. We asked whether a triple-redundant Monitor of Monitors System (MoMs) to collect and summarize key information from system-wide data sources could achieve high fault tolerance, early diagnosis of system failure, and improve data collection rates. In our Level I trauma center, patient vital signs(VS) monitors were networked to collect real time patient physiologic data streams from 94 bed units in our various resuscitation, operating, and critical care units. To minimize the impact of server collection failure, three BedMaster® VS servers were used in parallel to collect data from all bed units. To locate and diagnose system failures, we summarized critical information from high throughput datastreams in real-time in a dashboard viewer and compared the before and post MoMs phases to evaluate data collection performance as availability time, active collection rates, and gap duration, occurrence, and categories. Single-server collection rates in the 3-month period before MoMs deployment ranged from 27.8% to 40.5% with combined 79.1% collection rate. Reasons for gaps included collection server failure, software instability, individual bed setting inconsistency, and monitor servicing. In the 6-month post MoMs deployment period, average collection rates were 99.9%. A triple redundant patient data collection system with real-time diagnostic information summarization and representation improved the reliability of massive clinical data collection to nearly 100% in a Level I trauma center. Such data collection framework may also increase the automation level of hospital-wise information aggregation for optimal allocation of health care resources.

Identifiability and the applicability of data protection to big data

Big data is the present and the future. It holds the potential to make corporations create big revenues, to let scientists solve vital issues and to help governments make informed policy decisions. Yet the massive collection and use of personal data also raises a host of issues, particularly for individuals’ privacy and data protection rights. In Europe, data protection regulation is widely regarded as the answer to these issues. Through regulating data flows and providing individuals control over their personal data, some of big data’s possible undesired effects may be prevented. Yet upon closer scrutiny, it is uncertain whether European data protection legislation suffices to address these negative consequences. The practices in big data projects and the threshold for data to fall within the material scope of EU data protection legislation raise doubts about data protection’s applicability to big data.

The Case for Big Data: New York City's Kalvi HUMAN Project Aims to Use Big Data in Resolving Big Health Questions.

Cigarette smoking is tied to lung cancer, but people still smoke. Why do people start smoking in the first place? That is one of the many complex, interdisciplinary questions behind the Kavli HUMAN Project (http://kavlihumanproject.org), a massive data-collection endeavor with the goal of learning how everything-from biology to behavior and environment-affects the human condition.

Deletion Process_Only you can see my history: Investigating Digital Privacy, Digital Oblivion, and Control on Personal Data Through an Interactive Art Installation

In light of recent controversies surrounding massive data collection by corporations and government agencies, digital privacy, the right to oblivion, and data ownership have become increasingly important concerns. This paper describes the author’s artwork, Deletion Process_Only you can see my history, an interactive art installation based on her eight-year personal search history in the Google search engine. While the personal search history maintains a sense of privacy, according to the company’s own declaration, the author reveals this archive to viewers in order to raise awareness and provoke reflection on the aforementioned subjects. The author discusses her motivation, describes the making process and the decisions made at each step of designing the installation, while integrating at the same time a deeper discussion on the place of digital privacy and oblivion within the contemporary approach to art and technology.

Unmanned Aerial Vehicles for High-Throughput Phenotyping and Agronomic Research.

Advances in automation and data science have led agriculturists to seek real-time, high-quality, high-volume crop data to accelerate crop improvement through breeding and to optimize agronomic practices. Breeders have recently gained massive data-collection capability in genome sequencing of plants. Faster phenotypic trait data collection and analysis relative to genetic data leads to faster and better selections in crop improvement. Furthermore, faster and higher-resolution crop data collection leads to greater capability for scientists and growers to improve precision-agriculture practices on increasingly larger farms; e.g., site-specific application of water and nutrients. Unmanned aerial vehicles (UAVs) have recently gained traction as agricultural data collection systems. Using UAVs for agricultural remote sensing is an innovative technology that differs from traditional remote sensing in more ways than strictly higher-resolution images; it provides many new and unique possibilities, as well as new and unique challenges. Herein we report on processes and lessons learned from year 1—the summer 2015 and winter 2016 growing seasons–of a large multidisciplinary project evaluating UAV images across a range of breeding and agronomic research trials on a large research farm. Included are team and project planning, UAV and sensor selection and integration, and data collection and analysis workflow. The study involved many crops and both breeding plots and agronomic fields. The project’s goal was to develop methods for UAVs to collect high-quality, high-volume crop data with fast turnaround time to field scientists. The project included five teams: Administration, Flight Operations, Sensors, Data Management, and Field Research. Four case studies involving multiple crops in breeding and agronomic applications add practical descriptive detail. Lessons learned include critical information on sensors, air vehicles, and configuration parameters for both. As the first and most comprehensive project of its kind to date, these lessons are particularly salient to researchers embarking on agricultural research with UAVs.

Retos y tendencias sobre el futuro de la investigación acerca del aprendizaje con tecnologías digitales

The aim of this article is to analyse the challenges and trends in research on learning with digital technologies. In the last two decades, research on the use of digital technologies for education and learning has become an area of multidisciplinary research. The results show some limitations because it does not provide enough responses to analyse and design the appropriate teaching technological features to enhance learning. Often, research is trying to determine the benefits of a technology in learning using models that simplify reality. We believe it is time to consider the use of new methodologies and research tools that can improve the quality and results of investigations. The article discusses the formulation of research questions that are key to designing the studies. We describe the main research methods used and the use of technological infrastructure for the massive data collection. Finally, we analyse the importance of sharing data and results through the use of repositories for the dissemination of knowledge.

Normative Regulation of the Digital World: Summary of the Research Project IUF

As a new transnational territory of the law, the digital world has infatuated a multitude of public and private actors today, regardless of whether they pursue not-for-profit or for-profit goals. Omnipresent in social and economic life, digital technology raises many expectations, whether it is as: a lever of economic growth, a tool for governmental transparency and relationships between the administration and its citizens, a medium for many major applications -- in particular in the field of health or, finally, a favored means of communication connecting individuals all around the world.Nevertheless, this new space deserves deep thought with respect to the means of its normative regulation, still to be built. If specific legal rules have been adopted in order to govern the changes brought about by information and communication technologies, they are now insufficient to cope with the current and future potentials for connected things generating a massive collection of digital data. Their algorithmic processing will increase, at the risk of a governance by the numbers. Thus, the central question is what kind of democracy we want to build in this informational mass, control of which is escaping citizens. It is thus urgent to analyze the already-adopted legal rules, in particular in Europe and in North America, and to discover any inadequacies that they may present. In particular, it is essential to establish limits and responsibilities faced with a technology, the practices of which could partially escape man’s control or harm society’s weakest individuals.

Infrastructure planning and topology optimization for reliable mobile big data transmission under cloud radio access networks

With the development of user-centered and environment sensing technology of 5G, large capacity and ubiquitous coverage and massive data collection and processing will bring new challenges in wireless networks. The cloud radio access network (C-RAN) has been envisioned to provide a new wireless architecture for reliable transmission of mobile big data. In this paper, we focus on network planning deployment issue based on the optical mixed diet (OMD) technology. Specifically, the ring and spur topology optimization (RSTO) problem under the C-RAN architecture is investigated. The RSTO problem is formulated as a generic integer linear program (ILP) which can optimally (i) minimize the network deploying cost; (ii) identify the locations of Remote Radio Units (RRUs) and optical add-drop multiplexers (OADMs); (iii) identify the association relations between RRUs and OADMs; and (iv) satisfy the mobile coverage requirements so as to allow the mobile big data to be transmitted through the RRUs. We propose a new heuristic algorithm based on C-RAN architecture. Numerical results validate the ILP formulation and show the performance benefits of the proposed algorithm in terms of efficiency and effectiveness against Gurobi, which is an ILP solver. In numerical studies, we also demonstrate the performance benefits of the incorporation of CoMP technology in terms of total deployment cost reduction.

Healthy Predictions? Questions for Data Analytics in Health Care

The Patient Protection and Affordable Care Act (“Affordable Care Act” or ACA), health information technology (HIT) adoption, and increasing implementation of electronic medical records, are all propelling health care into the world of big data. Big data, analytics, and predictive algoithms are poised to play a large part in the transformation of health-care delivery in the United States, determining who will benefit and, unfortunately, who may suffer from its insights. Health-care reform depends on cost savings derived from the application of sophisticated data analytics to the ever-expanding mass of data collected from and about individual patients. Health data analytics can lead to improved care, new scientific discoveries, and better medical treatment. Encouraging healthy behaviors, eliminating health disparities, and addressing the underlying determinants of health in society are important national goals. It is unclear, however, whether massive data collection about personal health and individual social status, both within the health-care system and outside of it, will serve the goal of addressing historical discrimination in health care, or whether data analytics will lead to the loss of individual privacy, unequal treatment of individuals, and the perpetuation of health inequality. Data amassed from electronic health records (EHRs), private sector health website visits, personal health devices, mobile health applications, and social networks, are being linked together in a big data environment. Secondary use of health data by employers, insurers, marketers, and others heightens concerns. The collection and use of massive amounts of data about individuals, fed into a fragmented health analytics framework, may impose personal and societal costs if not carefully constructed. Furthermore, a predictive analytics environment in health care may affect some groups differently than others, not decreasing health dis-parities but segmenting populations and resulting in differential care. Health-care providers and policy makers should ask hard questions about how harms to personal privacy can be avoided, stigmas prevented, and threats of unbridled commercialization ameliorated.

Open-Source Sequence Clustering Methods Improve the State Of the Art.

Sequence clustering is a common early step in amplicon-based microbial community analysis, when raw sequencing reads are clustered into operational taxonomic units (OTUs) to reduce the run time of subsequent analysis steps. Here, we evaluated the performance of recently released state-of-the-art open-source clustering software products, namely, OTUCLUST, Swarm, SUMACLUST, and SortMeRNA, against current principal options (UCLUST and USEARCH) in QIIME, hierarchical clustering methods in mothur, and USEARCH's most recent clustering algorithm, UPARSE. All the latest open-source tools showed promising results, reporting up to 60% fewer spurious OTUs than UCLUST, indicating that the underlying clustering algorithm can vastly reduce the number of these derived OTUs. Furthermore, we observed that stringent quality filtering, such as is done in UPARSE, can cause a significant underestimation of species abundance and diversity, leading to incorrect biological results. Swarm, SUMACLUST, and SortMeRNA have been included in the QIIME 1.9.0 release. IMPORTANCE Massive collections of next-generation sequencing data call for fast, accurate, and easily accessible bioinformatics algorithms to perform sequence clustering. A comprehensive benchmark is presented, including open-source tools and the popular USEARCH suite. Simulated, mock, and environmental communities were used to analyze sensitivity, selectivity, species diversity (alpha and beta), and taxonomic composition. The results demonstrate that recent clustering algorithms can significantly improve accuracy and preserve estimated diversity without the application of aggressive filtering. Moreover, these tools are all open source, apply multiple levels of multithreading, and scale to the demands of modern next-generation sequencing data, which is essential for the analysis of massive multidisciplinary studies such as the Earth Microbiome Project (EMP) (J. A. Gilbert, J. K. Jansson, and R. Knight, BMC Biol 12:69, 2014, http://dx.doi.org/10.1186/s12915-014-0069-1).

Industrial Internet of Things-Based Collaborative Sensing Intelligence: Framework and Research Challenges.

The development of an efficient and cost-effective solution to solve a complex problem (e.g., dynamic detection of toxic gases) is an important research issue in the industrial applications of the Internet of Things (IoT). An industrial intelligent ecosystem enables the collection of massive data from the various devices (e.g., sensor-embedded wireless devices) dynamically collaborating with humans. Effectively collaborative analytics based on the collected massive data from humans and devices is quite essential to improve the efficiency of industrial production/service. In this study, we propose a collaborative sensing intelligence (CSI) framework, combining collaborative intelligence and industrial sensing intelligence. The proposed CSI facilitates the cooperativity of analytics with integrating massive spatio-temporal data from different sources and time points. To deploy the CSI for achieving intelligent and efficient industrial production/service, the key challenges and open issues are discussed, as well.

The Implications of Big Data Analytics on Business Intelligence: A Qualitative Study in China

Social media has brought about a revolution and dictated a paradigm shift in the operational strategies of firms globally. It has resulted in collection of massive data from a variety of social media channels, necessitating use of this data for business intelligence purposes. Despite its importance, little research exists on the implications of the use of Big Data analytics for business intelligence purposes. This study fills this gap in knowledge by examining the role and implication of Big Data analytics on business intelligence for the data collected from Social media channels in China. Given the exploratory nature of the research, the study takes a qualitative approach to data collection and analysis. Based on an extensive literature review, the study has developed a robust semi-structure questionnaire. We plan to conduct approximately 35-40 interviews with respondents such as IT managers, IT consultants, and Senior Business managers among others from a wide range of industries including retail and manufacturing settings. The data will be analysed using Nvivo to identify issues that are critical for creating value through Big Data analytics for business intelligence purposes. The results have significant impact for both theory and practice to devise plans and strategies to optimise the benefits of social-media channels for business value.

Elderly and fire safety: using serious games to elicit human behaviour in evacuation scenarios

The problem of leaving a building while facing some emergency has gained much attention from the scientific community. Evacuation simulators can help emergency planners to assess the safety of buildings. However, such simulators still lack behavioural knowledge to increase their accuracy. The elderly have specific behaviour idiosyncrasies. This paper presents a set of experiments designed to elicit human behaviour in evacuation scenarios using serious games, applied to a population sample of 20 adults aged over 65 years. They were presented three experiments (alarm id, exit-choice in five different situations and auditorium with four situations) and their reactions were collected. Results were analysed and compared with previous similar experiments. Future directions in this research include: to expand and to refine data collection to other scenarios and perform massive data collection using the internet; and to use this data to feed and enhance existing crowd simulators particularly for evacuation purposes.

Deserts in the Deluge: TerraPopulus and Big Human-Environment Data.

Terra Populus, or TerraPop, is a cyberinfrastructure project that integrates, preserves, and disseminates massive data collections describing characteristics of the human population and environment over the last six decades. TerraPop has made a number of GIScience advances in the handling of big spatial data to make information interoperable between formats and across scientific communities. In this paper, we describe challenges of these data, or 'deserts in the deluge' of data, that are common to spatial big data more broadly, and explore computational solutions specific to microdata, raster, and vector data models.

Framework for Social Network Data Mining

Social networks have become a vital component in personal life. People are addicted to social network features, updating their profile page and collaborating virtually with other members have become daily routines. Social networks contain massive collection of data. Web data mining is a new trend in the current research body. This conceptual paper introduces a framework that can be used to mine social network data. The proposed framework tries to handle the major limitations in current web mining frameworks by handling the unstructured and dynamic behavior of web data. Framework adopts the Hidden Markov Model to the data mining algorithm to predict the next status of web data.