Combine the superfast calculational capacities of Big Compute with the oceans of specific personal information comprising Big Data — and the fertile ground for computational propaganda emerges. That’s how the small AI programs called bots can be unleashed into cyberspace to target and deliver misinformation exactly to the people who will be most vulnerable to it. These messages can be refined over and over again based on how well they perform (again in terms of clicks, likes and so on). Worst of all, all this can be done semiautonomously, allowing the targeted propaganda (like fake news stories or faked images) to spread like viruses through communities most vulnerable to their misinformation.
According to Bolsover and Howard, viewing computational propaganda only from a technical perspective would be a grave mistake. As they explain, seeing it just in terms of variables and algorithms “plays into the hands of those who create it, the platforms that serve it, and the firms that profit from it.”
Computational propaganda is a new thing. People just invented it. And they did so by realizing possibilities emerging from the intersection of new technologies (Big Compute, Big Data) and new behaviors those technologies allowed (social media). But the emphasis on behavior can’t be lost.
People are not machines. We do things for a whole lot of reasons including emotions of loss, anger, fear and longing. To combat computational propaganda’s potentially dangerous effects on democracy in a digital age, we will need to focus on both its howand its why.
Based on the documents attached above, and the discussion and work already surrounding these documents, please consider the following flowchart:
study >>> systems theory >>> cybermetrics >>>
SWOT >>> strategic planning >>> task force >>> architect >>>
CM >>> public adviser >>> public polling >>> referendum
During the exercises surrounding the documents above, you have been introduced to various speakers / practitioners, who presented real-life cases regarding:
– the first goal of this technology instruction is to figure out the current state of technology in K12 settings.
assignment:
* split in groups * using each group member’s information and experience about technology in general and technology in school settings, use the flow chart above and identify any known technology, which can improve the process of each step in the flow chart.
* reconvene and compare results among groups. Find similarities and discrepancies and agree on a pool of applicable technology tools and concepts, which can improve the process reflected in the flow chart.
Example how to meet the requirements for the first goal: 1. based on your technological proficiency, how can you aid your study using system thinking/systems approach? the work ahead of you is collaborative. What collaborative tools do you know, which can help the team work across time and space? Skype, Google Hangouts for audio/video/desktopsharing. Google Drive/Docs for working on policies and similar text-based documents.
Work on the following assignment:
Trends in technology cannot be taken separately from other issues and are closely intertwined with other “big” trends :
keeping in mind this interdependence / balance, please work in groups on the following questions. Using the available links above and the literature they lead to, as well as your own findings, please provide your best opinion to these questions:
when planning for a new building and determining learning spaces, what is the percentage of importance, which we place on technology, in relation to furniture, for example?
how much do teachers have a say in the planning of the building, considering that they had worked and prefer “their type” of learning space?
who decides what technology and how? how one rationalizes the equation technology = learning spaces = available finances?
how much outsourcing (consulting) on any of the components of the equation above one can afford / consider? How much weight the strategic planning puts on the consulting (outsourcing) versus the internal opinion (staff and administrators)?
how “far in the future” your strategic plan is willing / able to look at, in terms of technology – learning spaces?
How to stay current with the technology developments:
Tim Brugger (Big Data): In part because the world around us is becoming “connected” through a growing number of IoT sensors, mobile devices, and the world’s affinity for the Internet, the sheer volume of information available is already staggering.
Daniel B. Kline (endless payment): While subscriptions have always been a factor on the enterprise side of the software business, they’re now moving into the consumer end of things. The leader has been Microsoft (NASDAQ:MSFT), which has managed to move a large part of its Office customer base into a subscription model.
Tim Green (budget smartphones): Zenfone 2 from Asus and the Moto G from Motorola.
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On behalf of the 2018 LITA Library Technology Forum Committee, I am pleased to notify you that your proposal, “Virtual Reality (VR) and Augmented Reality (AR) for Library Orientation: A Scalable Approach to Implementing VR/AR/MR in Education”, has been accepted for presentation at the 2018 LITA Library Technology Forum in Minneapolis, Minnesota (November 8-10).
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Mark Gill and Plamen Miltenoff will participate in a round table discussion Friday. November 9, 3:30PM at Haytt Regency, Minneapolis, MN. We will stream live on Facebook: https://www.facebook.com/InforMediaServices/
U of MN has a person, whose entire job is to read and negotiate contracts with vendors. No resources, not comfortable to negotiate contracts and vendors use this.
If you can’t open it, you don’t own it. if it is not ours… we don’t get what we don’t ask for.
libraries are now developing plenty, but if something is brought in, so stop analytics over people. Google Analytics collects data, which is very valuable for students. bring coherent rink of services around students and show money saving. it is not possible to make a number of copyright savings. collecting such data must be in the library, not outside. Data that is collected, will be put to use. Data that is collected, will be put to uses that challenge library values. Data puts people at risk. anonymized data is not anonymous. rethink our relationship to data. data sensitivity is contextual.
stop requiring MLSs for a lot of position. not PhDs in English, but people with specific skills.
perspective taking does not help you understand what others want. connection to tech. user testing – personas (imagining one’s perspective). we need to ask, better employ the people we want to understand. in regard of this, our profession is worse then other professions.
pay more is important to restore value of the profession.
Stanford – Folio, Cornell, Duke and several others. https://www.folio.org/ Alma too locked up for Stanford.
Easy Proxy for Alma Primo
Voyager to OCLC. Archive space from in-house to vendor. Migration
Polaris, payments, scheduling, PC sign up. Symphony, but discussing migration to Polaris to share ILS. COntent Diem. EasyProxy, from Millenium no Discovery Layer to Koha and EDS. ILL.
WMS to Alma. Illinois State – CARLY – from Voyager to Alma Primo. COntent Diem, Dynex to Koha.
Princeton: Voyager, migrating Alma and FOlio. Ex Libris. Finances migrate to PeopleSoft. SFX. Intota
RFPs – Request for Proposals stage. cloud and self-hosted bid.
Data Preparation. all data is standard, consistent. divorce package for vendors (preparing data to be exported (~10K). the less to migrate, the better, so prioritize chunks of data (clean up the data)
Data. overwhelming for the non-tech services. so a story is welcome. Design and Admin background, not librarian background, big picture, being not a librarian helps not stuck with the manusha (particular records)
teams and committees – how to compile a great team. who makes the decision. ORCHID integration. Blog or OneNote place to share information. touch base with everyone before they come to the meeting. the preplanning makes large meetings more productive.
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Using Design Thinking — Do we really want a makerspace?
Anne Shelley, Illinois State University, Milner Library
Julie Murphy, Illinois State University, Milner Library
Paul Unsbee, Illinois State University, Milner Library
makerbot replicator 3d printer
one touch studio 4 ready record studio. data analytics + several rooms to schedule.
Applications for the 2018 Institute will be accepted between December 1, 2017 and January 27, 2018. Scholars accepted to the program will be notified in early March 2018.
Title:
Learning to Harness Big Data in an Academic Library
Abstract (200)
Research on Big Data per se, as well as on the importance and organization of the process of Big Data collection and analysis, is well underway. The complexity of the process comprising “Big Data,” however, deprives organizations of ubiquitous “blue print.” The planning, structuring, administration and execution of the process of adopting Big Data in an organization, being that a corporate one or an educational one, remains an elusive one. No less elusive is the adoption of the Big Data practices among libraries themselves. Seeking the commonalities and differences in the adoption of Big Data practices among libraries may be a suitable start to help libraries transition to the adoption of Big Data and restructuring organizational and daily activities based on Big Data decisions. Introduction to the problem. Limitations
The redefinition of humanities scholarship has received major attention in higher education. The advent of digital humanities challenges aspects of academic librarianship. Data literacy is a critical need for digital humanities in academia. The March 2016 Library Juice Academy Webinar led by John Russel exemplifies the efforts to help librarians become versed in obtaining programming skills, and respectively, handling data. Those are first steps on a rather long path of building a robust infrastructure to collect, analyze, and interpret data intelligently, so it can be utilized to restructure daily and strategic activities. Since the phenomenon of Big Data is young, there is a lack of blueprints on the organization of such infrastructure. A collection and sharing of best practices is an efficient approach to establishing a feasible plan for setting a library infrastructure for collection, analysis, and implementation of Big Data.
Limitations. This research can only organize the results from the responses of librarians and research into how libraries present themselves to the world in this arena. It may be able to make some rudimentary recommendations. However, based on each library’s specific goals and tasks, further research and work will be needed.
Big Data is becoming an omnipresent term. It is widespread among different disciplines in academia (De Mauro, Greco, & Grimaldi, 2016). This leads to “inconsistency in meanings and necessity for formal definitions” (De Mauro et al, 2016, p. 122). Similarly, to De Mauro et al (2016), Hashem, Yaqoob, Anuar, Mokhtar, Gani and Ullah Khan (2015) seek standardization of definitions. The main connected “themes” of this phenomenon must be identified and the connections to Library Science must be sought. A prerequisite for a comprehensive definition is the identification of Big Data methods. Bughin, Chui, Manyika (2011), Chen et al. (2012) and De Mauro et al (2015) single out the methods to complete the process of building a comprehensive definition.
In conjunction with identifying the methods, volume, velocity, and variety, as defined by Laney (2001), are the three properties of Big Data accepted across the literature. Daniel (2015) defines three stages in big data: collection, analysis, and visualization. According to Daniel, (2015), Big Data in higher education “connotes the interpretation of a wide range of administrative and operational data” (p. 910) and according to Hilbert (2013), as cited in Daniel (2015), Big Data “delivers a cost-effective prospect to improve decision making” (p. 911).
The importance of understanding the process of Big Data analytics is well understood in academic libraries. An example of such “administrative and operational” use for cost-effective improvement of decision making are the Finch & Flenner (2016) and Eaton (2017) case studies of the use of data visualization to assess an academic library collection and restructure the acquisition process. Sugimoto, Ding & Thelwall (2012) call for the discussion of Big Data for libraries. According to the 2017 NMC Horizon Report “Big Data has become a major focus of academic and research libraries due to the rapid evolution of data mining technologies and the proliferation of data sources like mobile devices and social media” (Adams, Becker, et al., 2017, p. 38).
Power (2014) elaborates on the complexity of Big Data in regard to decision-making and offers ideas for organizations on building a system to deal with Big Data. As explained by Boyd and Crawford (2012) and cited in De Mauro et al (2016), there is a danger of a new digital divide among organizations with different access and ability to process data. Moreover, Big Data impacts current organizational entities in their ability to reconsider their structure and organization. The complexity of institutions’ performance under the impact of Big Data is further complicated by the change of human behavior, because, arguably, Big Data affects human behavior itself (Schroeder, 2014).
De Mauro et al (2015) touch on the impact of Dig Data on libraries. The reorganization of academic libraries considering Big Data and the handling of Big Data by libraries is in a close conjunction with the reorganization of the entire campus and the handling of Big Data by the educational institution. In additional to the disruption posed by the Big Data phenomenon, higher education is facing global changes of economic, technological, social, and educational character. Daniel (2015) uses a chart to illustrate the complexity of these global trends. Parallel to the Big Data developments in America and Asia, the European Union is offering access to an EU open data portal (https://data.europa.eu/euodp/home ). Moreover, the Association of European Research Libraries expects under the H2020 program to increase “the digitization of cultural heritage, digital preservation, research data sharing, open access policies and the interoperability of research infrastructures” (Reilly, 2013).
The challenges posed by Big Data to human and social behavior (Schroeder, 2014) are no less significant to the impact of Big Data on learning. Cohen, Dolan, Dunlap, Hellerstein, & Welton (2009) propose a road map for “more conservative organizations” (p. 1492) to overcome their reservations and/or inability to handle Big Data and adopt a practical approach to the complexity of Big Data. Two Chinese researchers assert deep learning as the “set of machine learning techniques that learn multiple levels of representation in deep architectures (Chen & Lin, 2014, p. 515). Deep learning requires “new ways of thinking and transformative solutions (Chen & Lin, 2014, p. 523). Another pair of researchers from China present a broad overview of the various societal, business and administrative applications of Big Data, including a detailed account and definitions of the processes and tools accompanying Big Data analytics. The American counterparts of these Chinese researchers are of the same opinion when it comes to “think about the core principles and concepts that underline the techniques, and also the systematic thinking” (Provost and Fawcett, 2013, p. 58). De Mauro, Greco, and Grimaldi (2016), similarly to Provost and Fawcett (2013) draw attention to the urgent necessity to train new types of specialists to work with such data. As early as 2012, Davenport and Patil (2012), as cited in Mauro et al (2016), envisioned hybrid specialists able to manage both technological knowledge and academic research. Similarly, Provost and Fawcett (2013) mention the efforts of “academic institutions scrambling to put together programs to train data scientists” (p. 51). Further, Asomoah, Sharda, Zadeh & Kalgotra (2017) share a specific plan on the design and delivery of a big data analytics course. At the same time, librarians working with data acknowledge the shortcomings in the profession, since librarians “are practitioners first and generally do not view usability as a primary job responsibility, usually lack the depth of research skills needed to carry out a fully valid” data-based research (Emanuel, 2013, p. 207).
Borgman (2015) devotes an entire book to data and scholarly research and goes beyond the already well-established facts regarding the importance of Big Data, the implications of Big Data and the technical, societal, and educational impact and complications posed by Big Data. Borgman elucidates the importance of knowledge infrastructure and the necessity to understand the importance and complexity of building such infrastructure, in order to be able to take advantage of Big Data. In a similar fashion, a team of Chinese scholars draws attention to the complexity of data mining and Big Data and the necessity to approach the issue in an organized fashion (Wu, Xhu, Wu, Ding, 2014).
Bruns (2013) shifts the conversation from the “macro” architecture of Big Data, as focused by Borgman (2015) and Wu et al (2014) and ponders over the influx and unprecedented opportunities for humanities in academia with the advent of Big Data. Does the seemingly ubiquitous omnipresence of Big Data mean for humanities a “railroading” into “scientificity”? How will research and publishing change with the advent of Big Data across academic disciplines?
Reyes (2015) shares her “skinny” approach to Big Data in education. She presents a comprehensive structure for educational institutions to shift “traditional” analytics to “learner-centered” analytics (p. 75) and identifies the participants in the Big Data process in the organization. The model is applicable for library use.
Being a new and unchartered territory, Big Data and Big Data analytics can pose ethical issues. Willis (2013) focusses on Big Data application in education, namely the ethical questions for higher education administrators and the expectations of Big Data analytics to predict students’ success. Daries, Reich, Waldo, Young, and Whittinghill (2014) discuss rather similar issues regarding the balance between data and student privacy regulations. The privacy issues accompanying data are also discussed by Tene and Polonetsky, (2013).
Privacy issues are habitually connected to security and surveillance issues. Andrejevic and Gates (2014) point out in a decision making “generated by data mining, the focus is not on particular individuals but on aggregate outcomes” (p. 195). Van Dijck (2014) goes into further details regarding the perils posed by metadata and data to the society, in particular to the privacy of citizens. Bail (2014) addresses the same issue regarding the impact of Big Data on societal issues, but underlines the leading roles of cultural sociologists and their theories for the correct application of Big Data.
Library organizations have been traditional proponents of core democratic values such as protection of privacy and elucidation of related ethical questions (Miltenoff & Hauptman, 2005). In recent books about Big Data and libraries, ethical issues are important part of the discussion (Weiss, 2018). Library blogs also discuss these issues (Harper & Oltmann, 2017). An academic library’s role is to educate its patrons about those values. Sugimoto et al (2012) reflect on the need for discussion about Big Data in Library and Information Science. They clearly draw attention to the library “tradition of organizing, managing, retrieving, collecting, describing, and preserving information” (p.1) as well as library and information science being “a historically interdisciplinary and collaborative field, absorbing the knowledge of multiple domains and bringing the tools, techniques, and theories” (p. 1). Sugimoto et al (2012) sought a wide discussion among the library profession regarding the implications of Big Data on the profession, no differently from the activities in other fields (e.g., Wixom, Ariyachandra, Douglas, Goul, Gupta, Iyer, Kulkami, Mooney, Phillips-Wren, Turetken, 2014). A current Andrew Mellon Foundation grant for Visualizing Digital Scholarship in Libraries seeks an opportunity to view “both macro and micro perspectives, multi-user collaboration and real-time data interaction, and a limitless number of visualization possibilities – critical capabilities for rapidly understanding today’s large data sets (Hwangbo, 2014).
The importance of the library with its traditional roles, as described by Sugimoto et al (2012) may continue, considering the Big Data platform proposed by Wu, Wu, Khabsa, Williams, Chen, Huang, Tuarob, Choudhury, Ororbia, Mitra, & Giles (2014). Such platforms will continue to emerge and be improved, with librarians as the ultimate drivers of such platforms and as the mediators between the patrons and the data generated by such platforms.
Every library needs to find its place in the large organization and in society in regard to this very new and very powerful phenomenon called Big Data. Libraries might not have the trained staff to become a leader in the process of organizing and building the complex mechanism of this new knowledge architecture, but librarians must educate and train themselves to be worthy participants in this new establishment.
Method
The study will be cleared by the SCSU IRB.
The survey will collect responses from library population and it readiness to use and use of Big Data. Send survey URL to (academic?) libraries around the world.
Data will be processed through SPSS. Open ended results will be processed manually. The preliminary research design presupposes a mixed method approach.
The study will include the use of closed-ended survey response questions and open-ended questions. The first part of the study (close ended, quantitative questions) will be completed online through online survey. Participants will be asked to complete the survey using a link they receive through e-mail.
Mixed methods research was defined by Johnson and Onwuegbuzie (2004) as “the class of research where the researcher mixes or combines quantitative and qualitative research techniques, methods, approaches, concepts, or language into a single study” (Johnson & Onwuegbuzie, 2004 , p. 17). Quantitative and qualitative methods can be combined, if used to complement each other because the methods can measure different aspects of the research questions (Sale, Lohfeld, & Brazil, 2002).
Sampling design
Online survey of 10-15 question, with 3-5 demographic and the rest regarding the use of tools.
1-2 open-ended questions at the end of the survey to probe for follow-up mixed method approach (an opportunity for qualitative study)
data analysis techniques: survey results will be exported to SPSS and analyzed accordingly. The final survey design will determine the appropriate statistical approach.
Project Schedule
Complete literature review and identify areas of interest – two months
Prepare and test instrument (survey) – month
IRB and other details – month
Generate a list of potential libraries to distribute survey – month
Contact libraries. Follow up and contact again, if necessary (low turnaround) – month
Collect, analyze data – two months
Write out data findings – month
Complete manuscript – month
Proofreading and other details – month
Significance of the work
While it has been widely acknowledged that Big Data (and its handling) is changing higher education (https://blog.stcloudstate.edu/ims?s=big+data) as well as academic libraries (https://blog.stcloudstate.edu/ims/2016/03/29/analytics-in-education/), it remains nebulous how Big Data is handled in the academic library and, respectively, how it is related to the handling of Big Data on campus. Moreover, the visualization of Big Data between units on campus remains in progress, along with any policymaking based on the analysis of such data (hence the need for comprehensive visualization).
This research will aim to gain an understanding on: a. how librarians are handling Big Data; b. how are they relating their Big Data output to the campus output of Big Data and c. how librarians in particular and campus administration in general are tuning their practices based on the analysis.
Based on the survey returns (if there is a statistically significant return), this research might consider juxtaposing the practices from academic libraries, to practices from special libraries (especially corporate libraries), public and school libraries.
References:
Adams Becker, S., Cummins M, Davis, A., Freeman, A., Giesinger Hall, C., Ananthanarayanan, V., … Wolfson, N. (2017). NMC Horizon Report: 2017 Library Edition.
Andrejevic, M., & Gates, K. (2014). Big Data Surveillance: Introduction. Surveillance & Society, 12(2), 185–196.
Asamoah, D. A., Sharda, R., Hassan Zadeh, A., & Kalgotra, P. (2017). Preparing a Data Scientist: A Pedagogic Experience in Designing a Big Data Analytics Course. Decision Sciences Journal of Innovative Education, 15(2), 161–190. https://doi.org/10.1111/dsji.12125
Bughin, J., Chui, M., & Manyika, J. (2010). Clouds, big data, and smart assets: Ten tech-enabled business trends to watch. McKinsey Quarterly, 56(1), 75–86.
Cohen, J., Dolan, B., Dunlap, M., Hellerstein, J. M., & Welton, C. (2009). MAD Skills: New Analysis Practices for Big Data. Proc. VLDB Endow., 2(2), 1481–1492. https://doi.org/10.14778/1687553.1687576
Daniel, B. (2015). Big Data and analytics in higher education: Opportunities and challenges. British Journal of Educational Technology, 46(5), 904–920. https://doi.org/10.1111/bjet.12230
Daries, J. P., Reich, J., Waldo, J., Young, E. M., Whittinghill, J., Ho, A. D., … Chuang, I. (2014). Privacy, Anonymity, and Big Data in the Social Sciences. Commun. ACM, 57(9), 56–63. https://doi.org/10.1145/2643132
De Mauro, A. D., Greco, M., & Grimaldi, M. (2016). A formal definition of Big Data based on its essential features. Library Review, 65(3), 122–135. https://doi.org/10.1108/LR-06-2015-0061
De Mauro, A., Greco, M., & Grimaldi, M. (2015). What is big data? A consensual definition and a review of key research topics. AIP Conference Proceedings, 1644(1), 97–104. https://doi.org/10.1063/1.4907823
Eaton, M. (2017). Seeing Library Data: A Prototype Data Visualization Application for Librarians. Publications and Research. Retrieved from http://academicworks.cuny.edu/kb_pubs/115
Emanuel, J. (2013). Usability testing in libraries: methods, limitations, and implications. OCLC Systems & Services: International Digital Library Perspectives, 29(4), 204–217. https://doi.org/10.1108/OCLC-02-2013-0009
Graham, M., & Shelton, T. (2013). Geography and the future of big data, big data and the future of geography. Dialogues in Human Geography, 3(3), 255–261. https://doi.org/10.1177/2043820613513121
Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Ullah Khan, S. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47(Supplement C), 98–115. https://doi.org/10.1016/j.is.2014.07.006
Laney, D. (2001, February 6). 3D Data Management: Controlling Data Volume, Velocity, and Variety.
Miltenoff, P., & Hauptman, R. (2005). Ethical dilemmas in libraries: an international perspective. The Electronic Library, 23(6), 664–670. https://doi.org/10.1108/02640470510635746
Philip Chen, C. L., & Zhang, C.-Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275(Supplement C), 314–347. https://doi.org/10.1016/j.ins.2014.01.015
Provost, F., & Fawcett, T. (2013). Data Science and its Relationship to Big Data and Data-Driven Decision Making. Big Data, 1(1), 51–59. https://doi.org/10.1089/big.2013.1508
Reyes, J. (2015). The skinny on big data in education: Learning analytics simplified. TechTrends: Linking Research & Practice to Improve Learning, 59(2), 75–80. https://doi.org/10.1007/s11528-015-0842-1
Schroeder, R. (2014). Big Data and the brave new world of social media research. Big Data & Society, 1(2), 2053951714563194. https://doi.org/10.1177/2053951714563194
Sugimoto, C. R., Ding, Y., & Thelwall, M. (2012). Library and information science in the big data era: Funding, projects, and future [a panel proposal]. Proceedings of the American Society for Information Science and Technology, 49(1), 1–3. https://doi.org/10.1002/meet.14504901187
Tene, O., & Polonetsky, J. (2012). Big Data for All: Privacy and User Control in the Age of Analytics. Northwestern Journal of Technology and Intellectual Property, 11, [xxvii]-274.
van Dijck, J. (2014). Datafication, dataism and dataveillance: Big Data between scientific paradigm and ideology. Surveillance & Society; Newcastle upon Tyne, 12(2), 197–208.
Waller, M. A., & Fawcett, S. E. (2013). Data Science, Predictive Analytics, and Big Data: A Revolution That Will Transform Supply Chain Design and Management. Journal of Business Logistics, 34(2), 77–84. https://doi.org/10.1111/jbl.12010
West, D. M. (2012). Big data for education: Data mining, data analytics, and web dashboards. Governance Studies at Brookings, 4, 1–0.
Willis, J. (2013). Ethics, Big Data, and Analytics: A Model for Application. Educause Review Online. Retrieved from https://docs.lib.purdue.edu/idcpubs/1
Wixom, B., Ariyachandra, T., Douglas, D. E., Goul, M., Gupta, B., Iyer, L. S., … Turetken, O. (2014). The current state of business intelligence in academia: The arrival of big data. CAIS, 34, 1.
Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. IEEE Transactions on Knowledge and Data Engineering, 26(1), 97–107. https://doi.org/10.1109/TKDE.2013.109
Wu, Z., Wu, J., Khabsa, M., Williams, K., Chen, H. H., Huang, W., … Giles, C. L. (2014). Towards building a scholarly big data platform: Challenges, lessons and opportunities. In IEEE/ACM Joint Conference on Digital Libraries (pp. 117–126). https://doi.org/10.1109/JCDL.2014.6970157
Here’s an easy way to explain IoT hacks to students:
A hacker accesses a device, like a webcam, through its internet connection. Devices with weak security or easy-to-guess passwords make easy targets.
The hacker can then infect the device with malware, a type of computer virus that takes control of a device.
The hacker now has a number of options. He or she can use the device to spy, infect other devices or attack a target like the servers (centralized computers that store network data) targeted in the October 2016 attack.
Research the manufacturer. Are they reputable? Have they previously been hacked? Big, established companies based in developed countries are usually the safest.
Read up on security features. Is the device password-protected? Can you set your own password? If so, make it a strong password that uses numbers, letters and symbols — avoid common words or phrases.
Regularly check for updates. Good companies will regularly update the software on their devices to protect against vulnerabilities.
Ask yourself — do you need it? Make sure internet-connectivity is something you really need on the device you’re using. In many cases, internet-connectivity is not necessary for the device to function properly.
a few tips that students can use to protect their privacy while using smartphones:
Research apps before signing up for them. Is it from a reputable developer? Has it had security issues in the past? Use the same approach as when researching IoT devices.
Look over the terms of service. What information does it require? Does it track or store your data? Can the developer sell your information? All of these questions are important to consider.
Be careful when linking apps to your social media accounts. Giving apps access to your social media accounts makes them vulnerable to hacking. Is there a good reason for the accounts to be linked? Can you sign up without linking to a social media account?
Use two-factor authentication. Two-factor authentication requires authorization beyond a password when using unrecognized devices such as entering a code sent to your cellphone. As apps allow, be sure to use two-factor authentication which will make it more difficult for hackers to access the information stored in your apps.
Document analysis is a systematic procedure for reviewing or evaluating documents—both printed and electronic (computer-based and Internet-transmitted) material. Like other analytical methods in qualitative research, document analysis requires that data be examined and interpreted in order to elicit meaning, gain understanding, and develop empirical knowledge(Corbin&Strauss,2008;seealsoRapley,2007).
Document analysis is often used in combination with other qualitative research methods as a means of triangulation—‘the combination of methodologies in the study of the same phenomenon’ (Denzin, 1970, p. 291)
The qualitative researcher is expected to draw upon multiple (at least two) sources of evidence; that is, to seek convergence and corroboration through the use of different data sources and methods. Apart from documents, such sources include interviews, participant or non-participant observation, and physical artifacts (Yin,1994).By triangulating data, the researcher attempts to provide ‘a confluence of evidence that breeds credibility’ (Eisner, 1991, p. 110). By examining information collected through different methods, the researcher can corroborate findings across data sets and thus reduce the impact of potential biases that can exist in a single study. According to Patton (1990), triangulation helps the researcher guard against the accusation that a study’s findings are simply an artifact of a single method, a single source, or a single investigator’s bias. Mixed-method studies (which combine quantitative and qualitative research techniques)sometimes include document analysis. Here is an example: In their large-scale, three-year evaluation of regional educational service agencies (RESAs), Rossman and Wilson (1985) combined quantitative and qualitative methods—surveys (to collect quantitative data) and open ended, semi structured interviews with reviews of documents (as the primary sources of qualitative data). The document reviews were designed to identify the agencies that played a role in supporting school improvement programs.
Document Analysis
Document analysis is a form of qualitative research in which documents are interpreted by the researcher to give voice and meaning around an assessment topic. Analyzing documents incorporates coding content into themes similar to how focus group or interview transcripts are analyzed. A rubric can also be used to grade or score a document. There are three primary types of documents:
• Public Records: The official, ongoing records of an organization’s activities. Examples include student transcripts, mission statements, annual reports, policy manuals, student handbooks, strategic plans, and syllabi.
• Personal Documents: First-person accounts of an individual’s actions, experiences, and beliefs. Examples include calendars, e-mails, scrapbooks, blogs, Facebook posts, duty logs, incident reports, reflections/journals, and newspapers.
• Physical Evidence: Physical objects found within the study setting (often called artifacts). Examples include flyers, posters, agendas, handbooks, and training materials.
As with all research, how you collect and analyse the data should depend on what you want to find out. Since you haven’t told us that, it is difficult to give you any precise advice. However, one really important matter in using documents as sources, whatever the overall aim of your research, is that data from documents are very different from data from speech events such as interviews, or overheard conversations.So the first analytic question you need to ask with regard to documents is ‘how are these data shaped by documentary production ?’ Something which differentiates nearly all data from documents from speech data is that those who compose documents know what comes at the end while still able to alter the beginning; which gives far more opportunity for consideration of how the recepient of the utterances will view the provider; ie for more artful self-presentation. Apart from this however, analysing the way documentary practice shapes your data will depend on what these documents are: for example your question might turn out to be ‘How are news stories produced ?’ – if you are using news reports, or ‘What does this bureaucracy consider relevant information (and what not relevant and what unmentionable) ? if you are using completed proformas or internal reports from some organisation.
An analysis technique is just like a hardware tool. It depends where and with what you are working to choose the right one. For a nail you should use a hammer, and there are lots of types of hammers to choose, depending on the type of nail.
So, in order to tell you the bettet technique, it is important to know the objectives you intend to reach and the theoretical framework you are using. Perhaps, after that, We could tell you if you should use content analysis, discourse or grounded theory (which type of it as, like the hammer, there are several types of GTs).
