Data Analytics Solutions Research Articles

Focus on analytical equipment, sensors, and detectors

Focus on analytical equipment, sensors, and detectors

Leveraging Fog Computing for Scalable IoT Datacenter Using Spine-Leaf Network Topology

With the Internet of Everything (IoE) paradigm that gathers almost every object online, huge traffic workload, bandwidth, security, and latency issues remain a concern for IoT users in today’s world. Besides, the scalability requirements found in the current IoT data processing (in the cloud) can hardly be used for applications such as assisted living systems, Big Data analytic solutions, and smart embedded applications. This paper proposes an extended cloud IoT model that optimizes bandwidth while allowing edge devices (Internet-connected objects/devices) to smartly process data without relying on a cloud network. Its integration with a massively scaled spine-leaf (SL) network topology is highlighted. This is contrasted with a legacy multitier layered architecture housing network services and routing policies. The perspective offered in this paper explains how low-latency and bandwidth intensive applications can transfer data to the cloud (and then back to the edge application) without impacting QoS performance. Consequently, a spine-leaf Fog computing network (SL-FCN) is presented for reducing latency and network congestion issues in a highly distributed and multilayer virtualized IoT datacenter environment. This approach is cost-effective as it maximizes bandwidth while maintaining redundancy and resiliency against failures in mission critical applications.

The digital future of oil and gas

O&G industry faces a new reality Currently, O&G companies are under intense pressure. Supply and demand dynamics triggered by increased unconventional production and slower global GDP, has led to significantly lower prices. Since 2014, price has fallen by around 70 percent and as a result, production profits are low and companies have been forced to reduce capital expenditures. Recent OPEC production cuts led to a slight recovery but mid-term forecast from most analysts is still 50% below 2014 prices. At the same time, global focus on environmental impact due to increasing CO2 emissions and global warming is forcing the industry to implement new technologies that operate more efficiently and reduce their carbon footprint. These complex challenges in the O&G market require a new way of thinking and a new way of doing business in order to stay competitive. O&G companies need to focus on reducing capital and operating costs without compromising HSSE standards and reliability. Digitalization has become an important competitive factor In this challenging time, the O&G industry can drive productivity and create value by taking advantage of new technologies and the concept of Digital Oil and Gas. Today, there are already an expansive set of proven digital solutions available that are being applied in various industries all over the world. Digital solutions turn data into real-time knowledge for faster, smarter decision making, which ultimately means reduced lifecycle and projects costs, as well as improved operational efficiency and safety. O&G companies are producing a large amount of data from their operations and process related activities. Digital life cycle software solutions, digital enabled integrated operational solutions, digital platforms, advanced data analytic solutions and data driven services so called Apps are helping customers harness their data to increase productivity, generate energy savings, optimize scheduling and increase plant security. This allows customers to comply with increasing HSSE requirements and improve the value of their capital expenditure. How can Digital Oil and Gas be leveraged to the O&G industry? Horizontal Industrial companies and OEMs are global pioneers in the field of industrial digitalization. They have set new standards with proven results across multiple industries. At the same time, they have decades of experience in the oil and gas industry. These ‘Digital’ OEMs understand the entire life cycle of O&G assets and have the necessary OEM equipment know-how. This know-how combined with their extensive analytics expertise, allows them to turn data into proven customer value every day.

Tapping the Value From Big Data Analytics

Management In a recent GE/Accenture report, surveys show that 81% of senior executives believe that big data analytics is one of the top three corporate priorities for the oil and gas industry through 2018. A striking finding was the sense of urgency felt by respondents about implementing data analytics solutions. This trend is driven primarily by current market conditions that are pushing companies to find new ways to become more efficient in exploration and production. In the quest by operators to become more efficient, many new initiatives are currently under way within organizations. Driven primarily by central excellence teams, objectives are to leverage both low- and high-resolution data (high-resolution data defined as data collected in seconds/minutes) to make better decisions quickly vs. a traditional approach of evaluating trends over a 12- or 24-hour period or after the fact using old reporting methods. Decision makers are convinced that if other industries such as airlines and consumer Internet players such as Amazon and Expedia can leverage big data to drive efficiency and growth, the same should and can apply to the oil and gas industry. Actionable Insights and Lower Costs If their assumptions are correct, leveraging hidden insights from mining data can help enterprise users make better, smarter decisions and reduce operational costs. However, as the industry pays closer attention to these initiatives, it is getting exposed to some harsh realities, including big data being uncharted territory for information technology (IT) and a company’s business side. Efforts to improve may actually lead to worse instead of better decision making if conducted using the wrong approaches. Further complicating the data analytics issue, most IT organizations are traditionally more familiar with process automation projects where business needs are known and stable. In contrast, data needs are context-dependent, dynamic, and may be unarticulated or even unknown sometimes. Solving this challenge requires anthropological skills that are in short supply in today’s IT world. Unfortunately, traditional requirements gathering fails when assessing data needs since the needs are fast-changing and diverse. Additionally, today’s machine data quality (especially on historical data) lacks accuracy, precision, completeness, and consistency for real-time analytics. As a practical matter, less than 50% of today’s enterprise users find information from corporate sources to be in a usable format. This problem will only get worse as the number of information sources, uses, and users continues to increase.

The data analytics industry and the promises of real-time knowing: perpetuating and deploying a rationality of speed

ABSTRACTThis article draws upon a sample of 34 data analytics providers in order to explore the rhetorical framing of the speediness of the data analytic solutions that they offer. General perceptions of cultural speed-up frame understandings of organisational life, against this backdrop of data analytics are presented as a potential solution to the need to speed-up and keep-up with the competition. As a result, it is argued that notions of speedy analytics are central to the spread and intensification of data-led decision-making, governance and ordering processes. The promises of real-time knowing are one means by which organisational speed and agility are seen to be achievable. The result is the pushing back of the limits of datafication. This article is concerned with the power of the data analytics industry and the powerful ways in which this industry presents and projects properties and promises onto data and data analytics. It suggests that this industry taps into, cultivates and then attempts to deploy the wider rationality of a need for speed.

A Framework-Based Approach to Utility Big Data Analytics

As advances in scientific and business data collection have exponentially created more data, electric utility companies are seeking new tools and techniques to turn the collected data into operational insights and assist with cost-saving decisions. The cost of building a specific business-driven big data application, however, can be tremendously high. This paper proposes developing a standard-based software framework to address key utility big data issues and foster development of big data analytical applications. Based on the support of this generic framework, new big data analytical solutions can be rapidly built and deployed to improve business practices in a utility organization. The proposed framework-based approach, as demonstrated in the conducted case studies, has proven to be promising for addressing the emerging big data challenges in the utility industry.

A query expansion approach for social media data extraction

Social media data analysis has been a significant topic of interest in both academia and industry over the past few years. The availability of the increasingly huge amount of social media data is changing our attitude towards social media data analytics which has moved from extracting more meaningful information to predicting social trends. Recently, an increasing number of approaches have been proposed on the applications of social media data. However, they mostly focus on processing the query result data which is often inadequate as the query terms are not easily determined by the users. Taking into consideration this shortcoming, this paper aims to examine the query side in order to provide a better solution for social media data analytics. We propose a Wikipedia-based query expansion and temporal ranking technique for social media data to extract more meaningful answers.

Big Data: challenges, opportunities and Cloud based solutions

<p class="AbstractText">We are living in an era of information explosion. There are challenges with large and complex amount of data generated every day by social networks, wikis, blogs, emails, traffic system, bridges, airplanes and engine, satellites and weather sensors. 90% of current data in the world has been created in the last two years. Our smart planet becomes more and more intelligent. Besides the challenges posed by such vast amount of data including storage, search, sharing, analysis, and visualization, there are also much opportunities for the world as it becomes more and more digitalized. This study presents Big Data and highlights its key concepts and state-of-the-art implementation as well as research challenges and suggests research directions for future. IT log analytics, Fraud detection pattern, social media pattern and modeling and management patterns are some of opportunities. Hadoop is a cloud based and open source solution for Big Data Analytics which has been written by java. Hadoop solution is currently still immature. In this paper, three topics are suggested for research direction: Security issues in Big Data, context-aware information retrieval, and integrating ontology with Big Data.</p>

A knowledge-based platform for Big Data analytics based on publish/subscribe services and stream processing

Big Data analytics is considered an imperative aspect to be further improved in order to increase the operating margin of both public and private enterprises, and represents the next frontier for their innovation, competition, and productivity. Big Data are typically produced in different sectors of the above organizations, often geographically distributed throughout the world, and are characterized by a large size and variety. Therefore, there is a strong need for platforms handling larger and larger amounts of data in contexts characterized by complex event processing systems and multiple heterogeneous sources, dealing with the various issues related to efficiently disseminating, collecting and analyzing them in a fully distributed way.In such a scenario, this work proposes a way to overcome two fundamental issues: data heterogeneity and advanced processing capabilities. We present a knowledge-based solution for Big Data analytics, which consists in applying automatic schema mapping to face with data heterogeneity, as well as ontology extraction and semantic inference to support innovative processing. Such a solution, based on the publish/subscribe paradigm, has been evaluated within the context of a simple experimental proof-of-concept in order to determine its performance and effectiveness.

Odyssey

We present a data analytics system, Odyssey, that is being developed at NEC Labs in collaboration with NEC’s commercial business units and our academic collaborators. The design principles of the system are based on the business requirements identified through extensive surveys and communications with the practitioners and customers. Most notable high-level requirements are: 1) The analytics system should be able to effectively use both structured and unstructured data sources. 2) Business requirements are not captured in a single simple metric but combination of metrics, such as value of data, performance, monetary costs, and they are dynamic. The system should manage data by observing constantly changing metric values. 3) Time-to-insight is very important, so the system should enable immediate exploratory querying of data without heavy prerequisite processes. 4) The system should efficiently support both ad-hoc queries and application workloads. 5) Very often there are already data analytics solutions / products in place (such as a traditional data warehouse), hence the system should be able to incorporate the existing settings. The data analysis is performed in a rapidly changing way and includes the new role of data scientist who is tasked with finding the benefit in big data, which come from disparate sources. The trend of collecting ever-growing data with unknown and unproven benefits, and the nature of the exploratory queries that are posed on these datasets represent an emerging type of data analysis. The fluid nature of this analysis is that the analyst may start by posing simple questions on the data but then evolve towards more sophisticated reasoning as well as apply sophisticated techniques. The evolutionary nature of the investigation is due to the analyst who may not initially be able to express her goals well, thus modifying her workflow slightly and iteratively refining it until achieving the intent. The evolutionary process may also require incorporating more data sources into the analysis to obtain richer and more confident answers. We call this iterative process by which an analyst finds bene-

Smart grid data analytics for digital protective relay event recordings

Information systems and intelligent smart grid data analytics will have a critical role in managing the massive amount of data becoming available in power system substations. Digital protective relays are multi-functional intelligent electronic devices based on microprocessors, which are being installed in substations throughout the power grid. New digital relays are replacing old-fashioned electro-mechanical or solid-state relays, and besides their protective function, they are coming equipped with monitoring capabilities. These monitoring capabilities are creating potential for better observability of power systems, redundancy in measurements, and improved decision-making process when operating the system. This article discusses the implementation requirements for a fully automated data analytics solution that provides data integration, fault analysis, and visualization based on the event data recorded by digital protective relays.

Mining the ‘Internet Graveyard’: Rethinking the Historians’ Toolkit

“Mining the Internet Graveyard” argues that the advent of massive quantity of born-digital historical sources necessitates a rethinking of the historians’ toolkit. The contours of a third wave of computational history are outlined, a trend marked by ever-increasing amounts of digitized information (especially web based), falling digital storage costs, a move to the cloud, and a corresponding increase in computational power to process these sources. Following this, the article uses a case study of an early born-digital archive at Library and Archives Canada – Canada’s Digital Collections project (CDC) – to bring some of these problems into view. An array of off-the-shelf data analysis solutions, coupled with code written in Mathematica, helps us bring context and retrieve information from a digital collection on a previously inaccessible scale. The article concludes with an illustration of the various computational tools available, as well as a call for greater digital literacy in history curricula and professional development.

Data mining-based big data analytics: parameters and layered framework

The growing data size and finding information’s from such bulk data need to be addressed. The term coined big data has found its vital importance in modern day business. With petabytes of data, it becomes essential that such new technologies need to be applied to the modern business for finding nuggets of the information hidden from it. The author felt the need to find the guiding parameters which can help the business users to understand at which stage need of big data and its relevant analytics is required and better for the organisation. These parameters will help the business user to enable them to find at what time they should be going for such technological changes in their setup. The blend of technologies which are needed and used to make a viable solution for the big data analytics in the organisation is high and this demands use of layered framework for effective implementation of the big data analytics using a most emerging analytical tool data mining as presented in this paper by the authors.

Does Big Data Matter to Value Creation? : 오라클(Oracle) 솔루션을 중심으로

It is essential that firm makes a rational and scientific decision making and creates a news value for the future direction. To do so, many firms attempt to collect meaningful data and find the filtered and refined implication for the better customer relationship and the active market drive through the various analytic tools. Among the possible IT solutions, utilization of 'Big Data' is becoming more attractive and necessary in such a way that it would help firms obtain the systemized and demanding information and facilitate their decision making process to keep up with the market needs. In this paper, it introduces the concepts and development of 'Big Data' recognized as a IT resource and solution under the rapidly changing firm environment. This study also presents the several firm cases using Big Data' and the Oracle's total data management and analytic solutions in order to support the application of 'Big Data'. Finally this paper provides a holistic viewpoint and realistic approach on use of 'Big Data' to create a new value.

Measures of Value: Data Analytics for Automated Fault Analysis

The power industry is experiencing an enormous expansion of computer and communication devices in substations. As a result, a massive amount of measurement data is being continuously collected, communicated, and processed. This is partly due to the need for much better monitoring capability as power system loading and complexity of operation have increased. The installation of a large number of intelligent electronic devices (IEDs) to accomplish the monitoring task has created new challenges, such as cyber and physical security, time-synchronized data storage, configuration management, and efficient visualization. Automated data analytics solutions are the key to efficient use of IED recordings. This automated process includes conversion of measurements to data, processing data to obtain information, and extraction of cause-and-effect knowledge. This article provides real-life implementation examples of data analytics developed to handle measurements from digital fault recorders (DFRs) and digital protective relays (DPRs). The discussion addresses the implementation challenges and business benefits of such solutions.

Data collapse, scaling functions, and analytical solutions of generalized growth models

We consider a nontrivial one-species population dynamics model with finite and infinite carrying capacities. Time-dependent intrinsic and extrinsic growth rates are considered in these models. Through the model per capita growth rate we obtain a heuristic general procedure to generate scaling functions to collapse data into a simple linear behavior even if an extrinsic growth rate is included. With this data collapse, all the models studied become independent from the parameters and initial condition. Analytical solutions are found when time-dependent coefficients are considered. These solutions allow us to perceive nontrivial transitions between species extinction and survival and to calculate the transition's critical exponents. Considering an extrinsic growth rate as a cancer treatment, we show that the relevant quantity depends not only on the intensity of the treatment, but also on when the cancerous cell growth is maximum.

Modified item tree analysis of inductive reasoning data

Recently, the coefficient k has been proposed for the selection among competing knowledge structure models for multinomial response data in Knowledge Space Theory, a modern psychometric test theory. This paper presents an application of the coefficient k to empirical data obtained from dichotomously scored inductive reasoning test items. A data set of binary scores for 366 male participants who responded to 27 test items measuring two types of inductive reasoning, ‘verbal analogy’ and ‘geometric-figural matrix completion’, is analyzed. An ‘optimal’ knowledge structure model is derived, selecting among 38 candidate competing models based on the criterion k. This is accomplished by a modified version of Item Tree Analysis, a data-analytic procedure for the derivation of knowledge structures on sets of dichotomous items. The data-analytic solution can be satisfactorily compared to theory-driven models obtained from psychological analyses of the inductive reasoning test items. Such a solution may then be utilized for the efficient computerized, adaptive assessment and training of the ability of inductive reasoning of the types of ‘verbal analogy’ and ‘geometric-figural matrix completion’.

Methodological challenges in research on sexual risk behavior: I. Item content, scaling, and data analytical options.

Investigation of sexual behavior involves many challenges, including how to assess sexual behavior and how to analyze the resulting data. Sexual behavior can be assessed using absolute frequency measures (also known as counts) or with relative frequency measures (e.g., rating scales that range from never to always). We discuss these 2 assessment approaches in the context of research on HIV risk behavior. We conclude that these 2 approaches yield nonredundant information and, more important, that only data yielding information about the absolute frequency of risk behavior have the potential to serve as valid indicators of HIV contraction risk. However, analyses of count data may be challenging because of non-normal distributions with many outliers. Therefore, we identify new and powerful data analytical solutions that have been developed recently to analyze count data and discuss limitations of a commonly applied method (viz., analysis of covariance using baseline scores as covariates).