Compelling topics on analytics of big data

  • Big data is defined as high volume, high variety/complexity, and high velocity, which is known as the 3Vs (Services, 2015).
  • Depending on the goal and objectives of the problem, that should help define which theories and techniques of big data analytics to use. Fayyad, Piatetsky-Shapiro, and Smyth (1996) defined that data analytics can be divided into descriptive and predictive analytics. Vardarlier and Silaharoglu (2016) agreed with Fayyad et al. (1996) division but added prescriptive analytics. Thus, these three divisions of big data analytics are:
    • Descriptive analytics explains “What happened?”
    • Predictive analytics explains “What will happen?”
    • Prescriptive analytics explains “Why will it happen?”
  • The scientific method helps give a framework for the data analytics lifecycle (Dietrich, 2013; Services, 2015). According to Dietrich (2013), it is a cyclical life cycle that has iterative parts in each of its six steps: discovery; pre-processing data; model planning; model building; communicate results, and
  • Data-in-motion is the real-time streaming of data from a broad spectrum of technologies, which also encompasses the data transmission between systems (Katal, Wazid, & Goudar, 2013; Kishore & Sharma, 2016; Ovum, 2016; Ramachandran & Chang, 2016). Data that is stored on a database system or cloud system is considered as data-at-rest and data that is being processed and analyzed is considered as data-in-use (Ramachandran & Chang, 2016).  The analysis of real-time streaming data in a timely fashion is also known as stream reasoning and implementing solutions for stream reasoning revolve around high throughput systems and storage space with low latency (Della Valle et al., 2016).
  • Data brokers are tasked collecting data from people, building a particular type of profile on that person, and selling it to companies (Angwin, 2014; Beckett, 2014; Tsesis, 2014). The data brokers main mission is to collect data and drop down the barriers of geographic location, cognitive or cultural gaps, different professions, or parties that don’t trust each other (Long, Cunningham, & Braithwaite, 2013). The danger of collecting this data from people can raise the incidents of discrimination based on race or income directly or indirectly (Beckett, 2014).
  • Data auditing is assessing the quality and fit for the purpose of data via key metrics and properties of the data (Techopedia, n.d.). Data auditing processes and procedures are the business’ way of assessing and controlling their data quality (Eichhorn, 2014).
  • If following an agile development processes the key stakeholders should be involved in all the lifecycles. That is because the key stakeholders are known as business user, project sponsor, project manager, business intelligence analyst, database administers, data engineer, and data scientist (Services, 2015).
  • Lawyers define privacy as (Richard & King, 2014): invasions into protecting spaces, relationships or decisions, a collection of information, use of information, and disclosure of information.
  • Richard and King (2014), describe that a binary notion of data privacy does not Data is never completely private/confidential nor completely divulged, but data lies in-between these two extremes.  Privacy laws should focus on the flow of personal information, where an emphasis should be placed on a type of privacy called confidentiality, where data is agreed to flow to a certain individual or group of individuals (Richard & King, 2014).
  • Fraud is deception; fraud detection is needed because as fraud detection algorithms are improving, the rate of fraud is increasing (Minelli, Chambers, &, Dhiraj, 2013). Data mining has allowed for fraud detection via multi-attribute monitoring, where it tries to find hidden anomalies by identifying hidden patterns through the use of class description and class discrimination (Brookshear & Brylow, 2014; Minellli et al., 2013).
  • High-performance computing is where there is either a cluster or grid of servers or virtual machines that are connected by a network for a distributed storage and workflow (Bhokare et al., 2016; Connolly & Begg, 2014; Minelli et al., 2013).
  • Parallel computing environments draw on the distributed storage and workflow on the cluster and grid of servers or virtual machines for processing big data (Bhokare et al., 2016; Minelli et al., 2013).
  • NoSQL (Not only Structured Query Language) databases are databases that are used to store data in non-relational databases i.e. graphical, document store, column-oriented, key-value, and object-oriented databases (Sadalage & Fowler, 2012; Services, 2015). NoSQL databases have benefits as they provide a data model for applications that require a little code, less debugging, run on clusters, handle large scale data and evolve with time (Sadalage & Fowler, 2012).
    • Document store NoSQL databases, use a key/value pair that is the file/file itself, and it could be in JSON, BSON, or XML (Sadalage & Fowler, 2012; Services, 2015). These document files are hierarchical trees (Sadalage & Fowler, 2012). Some sample document databases consist of MongoDB and CouchDB.
    • Graph NoSQL databases are used drawing networks by showing the relationship between items in a graphical format that has been optimized for easy searching and editing (Services, 2015). Each item is considered a node and adding more nodes or relationships while traversing through them is made simpler through a graph database rather than a traditional database (Sadalage & Fowler, 2012). Some sample graph databases consist of Neo4j Pregel, etc. (Park et al., 2014).
    • Column-oriented databases are perfect for sparse datasets, ones with many null values and when columns do have data the related columns are grouped together (Services, 2015). Grouping demographic data like age, income, gender, marital status, sexual orientation, etc. are a great example for using this NoSQL database. Cassandra is an example of a column-oriented database.
  • Public cloud environments are where a supplier to a company provides a cluster or grid of servers through the internet like Spark AWS, EC2 (Connolly & Begg, 2014; Minelli et al. 2013).
  • A community cloud environment is a cloud that is shared exclusively by a set of companies that share the similar characteristics, compliance, security, jurisdiction, etc. (Connolly & Begg, 2014).
  • Private cloud environments have a similar infrastructure to a public cloud, but the infrastructure only holds the data one company exclusively, and its services are shared across the different business units of that one company (Connolly & Begg, 2014; Minelli et al., 2013).
  • Hybrid clouds are two or more cloud structures that have either a private, community or public aspect to them (Connolly & Begg, 2014).
  • Cloud computing allows for the company to purchase the services it needs, without having to purchase the infrastructure to support the services it might think it will need. This allows for hyper-scaling computing in a distributed environment, also known as hyper-scale cloud computing, where the volume and demand of data explode exponentially yet still be accommodated in public, community, private, or hybrid cloud in a cost efficiently (Mainstay, 2016; Minelli et al., 2013).
  • Building block system of big data analytics involves a few steps Burkle et al. (2001):
    • What is the purpose that the new data will and should serve
      • How many functions should it support
      • Marking which parts of that new data is needed for each function
    • Identify the tool needed to support the purpose of that new data
    • Create a top level architecture plan view
    • Building based on the plan but leaving room to pivot when needed
      • Modifications occur to allow for the final vision to be achieved given the conditions at the time of building the architecture.
      • Other modifications come under a closer inspection of certain components in the architecture

 

References

  • Angwin, J. (2014). Privacy tools: Opting out from data brokers. Pro Publica. Retrieved from https://www.propublica.org/article/privacy-tools-opting-out-from-data-brokers
  • Beckett, L. (2014). Everything we know about what data brokers know about you. Pro Publica. Retrieved from https://www.propublica.org/article/everything-we-know-about-what-data-brokers-know-about-you
  • Bhokare, P., Bhagwat, P., Bhise, P., Lalwani, V., & Mahajan, M. R. (2016). Private Cloud using GlusterFS and Docker.International Journal of Engineering Science5016.
  • Brookshear, G., & Brylow, D. (2014). Computer Science: An Overview, (12th). Pearson Learning Solutions. VitalBook file.
  • Burkle, T., Hain, T., Hossain, H., Dudeck, J., & Domann, E. (2001). Bioinformatics in medical practice: what is necessary for a hospital?. Studies in health technology and informatics, (2), 951-955.
  • Connolly, T., Begg, C. (2014). Database Systems: A Practical Approach to Design, Implementation, and Management, (6th). Pearson Learning Solutions. [Bookshelf Online].
  • Della Valle, E., Dell’Aglio, D., & Margara, A. (2016). Tutorial: Taming velocity and variety simultaneous big data and stream reasoning. Retrieved from https://pdfs.semanticscholar.org/1fdf/4d05ebb51193088afc7b63cf002f01325a90.pdf
  • Dietrich, D. (2013). The genesis of EMC’s data analytics lifecycle. Retrieved from https://infocus.emc.com/david_dietrich/the-genesis-of-emcs-data-analytics-lifecycle/
  • Eichhorn, G. (2014). Why exactly is data auditing important? Retrieved from http://www.realisedatasystems.com/why-exactly-is-data-auditing-important/
  • Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI Magazine, 17(3), 37. Retrieved from: http://www.aaai.org/ojs/index.php/aimagazine/article/download/1230/1131/
  • Katal, A., Wazid, M., & Goudar, R. H. (2013, August). Big data: issues, challenges, tools and good practices. InContemporary Computing (IC3), 2013 Sixth International Conference on (pp. 404-409). IEEE.
  • Kishore, N. & Sharma, S. (2016). Secure data migration from enterprise to cloud storage – analytical survey. BIJIT-BVICAM’s Internal Journal of Information Technology. Retrieved from http://bvicam.ac.in/bijit/downloads/pdf/issue15/09.pdf
  • Long, J. C., Cunningham, F. C., & Braithwaite, J. (2013). Bridges, brokers and boundary spanners in collaborative networks: a systematic review.BMC health services research13(1), 158.
  • (2016). An economic study of the hyper-scale data center. Mainstay, LLC, Castle Rock, CO, the USA, Retrieved from http://cloudpages.ericsson.com/ transforming-the-economics-of-data-center
  • Minelli, M., Chambers, M., &, Dhiraj, A. (2013). Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today’s Businesses. John Wiley & Sons P&T. [Bookshelf Online].
  • Ovum (2016). 2017 Trends to watch: Big Data. Retrieved from http://info.ovum.com/uploads/files/2017_Trends_to_Watch_Big_Data.pdf
  • Park, Y., Shankar, M., Park, B. H., & Ghosh, J. (2014, March). Graph databases for large-scale healthcare systems: A framework for efficient data management and data services. In Data Engineering Workshops (ICDEW), 2014 IEEE 30th International Conference on (pp. 12-19). IEEE.
  • Ramachandran, M. & Chang, V. (2016). Toward validating cloud service providers using business process modeling and simulation. Retrieved from http://eprints.soton.ac.uk/390478/1/cloud_security_bpmn1%20paper%20_accepted.pdf
  • Richards, N. M., & King, J. H. (2014). Big Data Ethics. Wake Forest Law Review, 49, 393–432.
  • Sadalage, P. J., Fowler, M. (2012). NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence, 1st Edition. [Bookshelf Online].
  • Services, E. E. (2015). Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, (1st). [Bookshelf Online].
  • Technopedia (n.d.). Data audit. Retrieved from https://www.techopedia.com/definition/28032/data-audit
  • Tsesis, A. (2014). The right to erasure: Privacy, data brokers, and the indefinite retention of data.Wake Forest L. Rev.49, 433.
  • Vardarlier, P., & Silahtaroglu, G. (2016). Gossip management at universities using big data warehouse model integrated with a decision support system. International Journal of Research in Business and Social Science, 5(1), 1–14. Doi: http://doi.org/10.1108/ 17506200710779521
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Cloud computing and big data

High-performance computing is where there is either a cluster and grid of servers or virtual machines that are connected by a network for a distributed storage and workflow (Bhokare et al., 2016; Connolly & Begg, 2014; Minelli, Chamber, & Dhiraj, 2013). Parallel computing environments draw on the distributed storage and workflow on the cluster and grid of servers or virtual machines for processing big data (Bhokare et al., 2016; Minelli et al., 2013). NoSQL databases have benefits as they provide a data model for applications that require a little code, less debugging, run on clusters, handle large scale data, stored across distributed systems, use parallel processing, and evolve with time (Sadalage & Fowler, 2012).  Cloud technology is the integration of data storage across a distributed set of servers or virtual machines through either traditional relational database systems or NoSQL database systems while allowing for data preprocessing and processing through parallel processing (Bhokare et al., 2016; Connolly & Begg, 2014; Minelli et al., 2013; Sadalage & Folwer, 2012).

Clouds can come in different flavors depending on how much the organization and supplier want to manage: Infrastructure as a Service, Platform as a Service, and Software as a Service (Connolly & Begg, 2014).  Thus, this makes the enterprise IT act as a broker across the various cloud options.  Also, analyzing exactly how and where data are stored to ensure it complies with various national and international data rules and regulations while preserving data privacy exist with the type of cloud use: public, community, private and hybrid clouds (Minelli et al. 2013; Conolloy & Begg, 2014).

Public cloud environments are where a supplier to a company provides a cluster or grid of servers through the internet like Spark AWS, EC2 (Connolly & Begg, 2014; Minelli et al. 2013).  Cloud computing can be thought of as a set of building blocks.  The company can grow or shrink a number of servers and services when needed dynamically, which allows the company to request the right amount of services for their data collection, storage, preprocessing, and processing needs (Bhokare et al., 2016; Minelli et al., 2013; Sadalage & Fowler, 2012).  This allows for the company to purchase the services it needs, without having to purchase the infrastructure to support the services it might think it will need. This allows for hyper-scaling computing in a distributed environment, also known as hyper-scale cloud computing, where the volume and demand of data explode exponentially yet still be accommodated in public, community, private, or hybrid cloud in a cost efficiently (Mainstay, 2016; Minelli et al., 2013).

Data storage and sharing are a key component of using enterprise public clouds (Sumana & Biswal, 2016).  However, it should be noted that the data is stored in the public cloud is stored on the same servers as probably the company’s competitors, so data security is an issue. Sumana and Biswal (2016) proposed that a key aggregate cryptosystem to be used, where the enterprise holds the master key for all its enterprise files, whereas going a deep layer users can have other data encrypted to send within the enterprise, without needing to know the enterprise file key. This proposed solution for data security in a public cloud allows for end-user registration, end-user revocation, file generation and deletion, and file access and traceability.

A community cloud environment is a cloud that is shared exclusively by a set of companies that share the similar characteristics, compliance, security, jurisdiction, etc. (Connolly & Begg, 2014). Thus, the infrastructure of all of these servers and grids meet industry standards and best practices, with the shared cost of the infrastructure is maintained by the community.

Private cloud environments have a similar infrastructure to a public cloud, but the infrastructure only holds the data one company exclusively, and its services are shared across the different business units of that one company (Connolly & Begg, 2014; Minelli et al., 2013). An organization may have all the components already to build a cloud through various on-premise computing resources and thus tend to build a cloud system using open source code on their internal infrastructure; this is called an on-premise private cloud (Bhokare et al., 2016). The benefit of the private cloud is full control of your data, and the cost of the servers are spread across all the business units, but the infrastructure costs (initial, upgrades, and maintenance costs) are in the company.

Hybrid clouds are two or more cloud structures that have either a private, community or public aspect to them (Connolly & Begg, 2014).  This allows for some data to be retained in the house if need be, and reducing the size of capital expenditure for the internal cloud infrastructure, while other data is stored externally where the cost of the infrastructure is not directly felt by the organization.

References

  • Bhokare, P., Bhagwat, P., Bhise, P., Lalwani, V., & Mahajan, M. R. (2016). Private Cloud using GlusterFS and Docker.International Journal of Engineering Science5016.
  • Connolly, T., Begg, C. (2014). Database Systems: A Practical Approach to Design, Implementation, and Management, (6th). Pearson Learning Solutions. [Bookshelf Online].
  • (2016). An economic study of the hyper-scale data center. Mainstay, LLC, Castle Rock, CO, the USA, Retrieved from http://cloudpages.ericsson.com/ transforming-the-economics-of-data-center
  • Minelli, M., Chambers, M., &, Dhiraj, A. (2013). Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today’s Businesses. John Wiley & Sons P&T. [Bookshelf Online].
  • Sadalage, P. J., Fowler, M. (2012). NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence, [Bookshelf Online].
  • Sumana, P., & Biswal, B. K. (2016). Secure Privacy Protected Data Sharing Between Groups in Public Cloud.International Journal of Engineering Science3285.

Big Data Analytics: Compelling Topics

Big Data and Hadoop:

According to Gray et al. (2005), traditional data management relies on arrays and tables in order to analyze objects, which can range from financial data, galaxies, proteins, events, spectra data, 2D weather, etc., but when it comes to N-dimensional arrays there is an “impedance mismatch” between the data and the database.    Big data, can be N-dimensional, which can also vary across time, i.e. text data (Gray et al., 2005). Big data, by its name, is voluminous. Thus, given the massive amounts of data in Big Data that needs to get processed, manipulated, and calculated upon, parallel processing and programming are there to use the benefits of distributed systems to get the job done (Minelli, Chambers, & Dhiraj, 2013).  Parallel processing allows making quick work on a big data set, because rather than having one processor doing all the work, you split up the task amongst many processors.

Hadoop’s Distributed File System (HFDS), breaks up big data into smaller blocks (IBM, n.d.), which can be aggregated like a set of Legos throughout a distributed database system. Data blocks are distributed across multiple servers. Hadoop is Java-based and pulls on the data that is stored on their distributed servers, to map key items/objects, and reduces the data to the query at hand (MapReduce function). Hadoop is built to deal with big data stored in the cloud.

Cloud Computing:

Clouds come in three different privacy flavors: Public (all customers and companies share the all same resources), Private (only one group of clients or company can use a particular cloud resources), and Hybrid (some aspects of the cloud are public while others are private depending on the data sensitivity.  Cloud technology encompasses Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS).  These types of cloud differ in what the company managers on what is managed by the cloud provider (Lau, 2011).  Cloud differs from the conventional data centers where the company managed it all: application, data, O/S, virtualization, servers, storage, and networking.  Cloud is replacing the conventional data center because infrastructure costs are high.  For a company to be spending that much money on a conventional data center that will get outdated in 18 months (Moore’s law of technology), it’s just a constant sink in money.  Thus, outsourcing the data center infrastructure is the first step of company’s movement into the cloud.

Key Components to Success:

You need to have the buy-in of the leaders and employees when it comes to using big data analytics for predictive, prescriptive or descriptive purposes.  When it came to buy-in, Lt. Palmer had to nurture top-down support as well as buy-in from the bottom-up (ranks).  It was much harder to get buy-in from more experienced detectives, who feel that the introduction of tools like analytics, is a way to tell them to give up their long-standing practices and even replace them.  So, Lt. Palmer had sold Blue PALMS as “What’s worked best for us is proving [the value of Blue PALMS] one case at a time, and stressing that it’s a tool, that it’s a compliment to their skills and experience, not a substitute”.  Lt. Palmer got buy-in from a senior and well-respected officer, by helping him solve a case.  The senior officer had a suspect in mind, and after feeding in the data, the tool was able to predict 20 people that could have done it in an order of most likely.  The suspect was on the top five, and when apprehended, the suspect confessed.  Doing, this case by case has built the trust amongst veteran officers and thus eventually got their buy in.

Applications of Big Data Analytics:

A result of Big Data Analytics is online profiling.  Online profiling is using a person’s online identity to collect information about them, their behaviors, their interactions, their tastes, etc. to drive a targeted advertising (McNurlin et al., 2008).  Profiling has its roots in third party cookies and profiling has now evolved to include 40 different variables that are collected from the consumer (Pophal, 2014).  Online profiling allows for marketers to send personalized and “perfect” advertisements to the consumer, instantly.

Moving from online profiling to studying social media, He, Zha, and Li (2013) stated their theory, that with higher positive customer engagement, customers can become brand advocates, which increases their brand loyalty and push referrals to their friends, and approximately 1/3 people followed a friend’s referral if done through social media. This insight came through analyzing the social media data from Pizza Hut, Dominos and Papa Johns, as they aim to control more of the market share to increase their revenue.  But, is this aiding in protecting people’s privacy when we analyze their social media content when they interact with a company?

HIPAA described how we should conduct de-identification of 18 identifiers/variables that would help protect people from ethical issues that could arise from big data.   HIPAA legislation is not standardized for all big data applications/cases; it is good practice. However, HIPAA legislation is mostly concerned with the health care industry, listing those 18 identifiers that have to be de-identified: Names, Geographic data, Dates, Telephone Numbers, VIN, Fax, Device ID and serial numbers, emails addresses, URLs, SSN, IP address, Medical Record Numbers, Biometric ID (fingerprints, iris scans, voice prints, etc), full face photos, health plan beneficiary numbers, account numbers, any other unique ID number (characteristic, codes, etc), and certifications/license numbers (HHS, n.d.).  We must be aware that HIPAA compliance is more a feature of the data collector and data owner than the cloud provider.

HIPAA arose from the human genome project 25 years ago, where they were trying to sequence its first 3B base pair of the human genome over a 13 year period (Green, Watson, & Collins, 2015).  This 3B base pair is about 100 GB uncompressed and by 2011, 13 quadrillion bases were sequenced (O’Driscoll et al., 2013). Studying genomic data comes with a whole host of ethical issues.  Some of those were addressed by the HIPPA legislation while other issues are left unresolved today.

One of the ethical issues that arose were mentioned in McEwen et al. (2013), for people who have submitted their genomic data 25 years ago can that data be used today in other studies? What about if it was used to help the participants of 25 years ago to take preventative measures for adverse health conditions?  However, ethical issues extend beyond privacy and compliance.  McEwen et al. (2013) warn that data has been collected for 25 years, and what if data from 20 years ago provides data that a participant can suffer an adverse health condition that could be preventable.  What is the duty of the researchers today to that participant?

Resources:

Big Data Analytics: Cloud Computing

Clouds come in three different privacy flavors: Public (all customers and companies share the all same resources), Private (only one group of clients or company can use a particular cloud resources), and Hybrid (some aspects of the cloud are public while others are private depending on the data sensitivity.

Cloud technology encompasses Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS).  These types of cloud differ in what the company managers with respect to what is managed by the cloud provider.  For IaaS the company manages the applications, data, runtime, and middleware, whereas the provider administers the O/S, virtualization, servers, storage, and networking.  For PaaS the company manages the applications, and data, whereas the vendor, administers the runtime, middleware, O/S, virtualization, servers, storage, and networking.  Finally SaaS the provider manages it all: application, data, O/S, virtualization, servers, storage, and networking (Lau, 2011).  This differs from the conventional data centers where the company managed it all: application, data, O/S, virtualization, servers, storage, and networking.

Examples of IaaS are Amazon Web Services, Rack Space, and VMware vCloud.  Examples of PaaS are Google App Engine, Windows Azure Platform, and force.com. Examples of SaaS are Gmail, Office 365, and Google Docs (Lau, 2011).

There are benefits of cloud is this pay-as-you-go business model.  One, the company can pay for as much (SaaS) or as little (IaaS) of the service that they need and how much in space they require. Two, the company can go on an On-Demand model, which businesses can scale up and down as they need (Dikaiakos, Katsaros, Mehra, Pallis, & Vakali, 2009).  For example, if a company would like a development environment for 3 weeks, they can build it up in the cloud for that time period and spend money for using the service for 3 weeks rather than buying a new set of infrastructure and setting up all the libraries.  This can help speed up the development speed in a ton of applications moving forward when you elect the cloud versus buying a new infrastructure.  These models are like renting a car.  Renting a car for what you need, but you are paying for what you use (Lau, 2011).

Replacing Conventional Data Center?

Infrastructure costs are really high.  For a company to be spending that much money on something that will get outdated in 18 months (Moore’s law of technology), it’s just a constant sink in money.  Outsourcing, infrastructure is the first step of company’s movement into the cloud.  However, companies need to understand the different privacy flavors well, because if data is stored in a public cloud, it will be hard to destroy the hardware, because you will destroy not only your data, but other people’s and company’s data.  Private clouds are best for government agencies which may need or require physical destruction of the hardware.  Government agencies may even use hybrid structures, keeping private data in the private clouds and the public stuff in a public cloud.  Companies that contract with the government could migrate to hybrid clouds in the future, and businesses without contracts with the government could go onto a public cloud.  There may always be a need to store the data on a private server, like patents, of KFC’s 7 herbs and spices recipe, but for the majority of the data, personally the cloud may be a grand place to store and work off of.

Note: Companies that do venture into moving into a cloud platform and storing data, they should focus on migrating data and data dictionaries slowly and with uniformity.  Data variables should have the same naming convention, one definition, a list of who is responsible for the data, meta-data, etc.  This would be a great chance for companies, while in migration to a new infrastructure to clean up their data.

Resources: