introduction
Dr. Priyadarshy is Chief Data Scientist at Halliburton (NYSE: HAL). He is also Senior Fellow of International Cyber Security Center at George Mason University. He is a veteran of Big Data and Data Science. He is a global leader often referred as Venture Idealist, who transforms businesses by leveraging Big Data, Data Science, and Emerging Technologies. He has provided executive consulting to Global companies. Some of his recent associations include established company like BellSystem24 in Tokyo, mPortal (Virginia) and Internet Startups like Enformed (San Francisco), Foodem, Sustainable Star, and Innoengineer (in the DC Metro area), etc.
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Book Speech
Recently, he was the VP of Data Science, at Acxiom Corporation (NASDAQ: ACXM), in their Industry Principals Group at division within the Global Consulting. His clients included Fortune 100 companies in various verticals in US and Australia.

Prior to that Dr. Priyadarshy was at Network Solutions as the Chief Knowledge officer and Vice President of Analytics. A direct report to the Chairman and CEO of Network Solutions and a member of the senior leadership team, Satyam provided strategic leadership to improve products and services. Satyam led the transformation of the main revenue channel the, ecommerce storefront, from Web 1.0 to Web 2.0 technologies. He provided leadership to web analytics, marketing analytics and custom analytics teams. He defined and implemented usability research, competitive intelligence and knowledge management structures using open sources technologies and solutions.

His key accomplishments include the alternative sourcing strategy using agile, adaptable and cost-effective outsourced global partners, 75% reduction in cost and 300% increase in productivity; deployment of the software as a service (SaaS) model for increased productivity, re-evaluation of the existing ROI models for search engine marketing leading to savings of millions of dollar per year and; innovative solutions for resolving issues raised by customers, by performing holistic analysis of business, technology and voice of customer data.

Prior to Network Solutions, Satyam led RKR Group, a boutique consulting company, focused on CEOs and CTOs of a variety of public (comScore) and private Internet and telecom companies.

Before RKR Group, Satyam spent 9 years at AOL, a leading Internet and media company, in various roles: as Co-Founder and Senior Research Scientist of AOL Labs, as Head of Research Labs-India, as Technical Expert for real-time performance and customer experience and product improvement, and as a hands-on technical professional running extremely heavy transactional web properties with over 25 millions user per month, in a 24xForever environment, utilizing Six-Sigma processes to reduce downtime to zero. Dr. Priyadarshy led the technical strategy and architecture of customer experience data collected around the clock from across the world, for products and services including web sites, portals, clients, audio, video and advertisements., from innovatively designed set-top computers.

Previous to AOL, Dr. Priyadarshy held various scientific and academic positions at Rutgers, University of Pittsburgh, and University of Sydney, Australia. He has been a principal/co-investigator of 7 different high-performance supercomputing research projects.

Satyam exhibits strong leadership qualities enabling him to assemble successful teams by leveraging global talent. He exhibits a breadth of scientific knowledge, in-depth technology experience, and extensive business acumen. Expert in emerging technology and its impact on business, he is a uniquely valuable leader to companies seeking to leverage technology. A frequent invited speaker to leading technology, science and business conferences, and an author of over 30 research papers and articles in reputable international journals and magazines. Satyam remains on the cutting edge of the technology thought leadership and execution.
UPCOMING SPEECH
work & consulting experience
invited articles
DNA: insulator or wire?
DNA-based electron transfer reactions are seen in processes such as biosynthesis and radiation damage/repair, but are poorly understood. What kinds of experiments might tell us how far and how fast electrons can travel in DNA? What does modern theory predict? Electron transfer reactions involving DNA are clearly important in nucleic acid biosynthesis, regulation, damage, and repair. Studies of electron transfer between species bound covalently and noncovalently to DNA have given provocative results, and are at a similar stage to that reached several years ago by the protein electron transfer community [l-3]. Electron transfer has been shown to proceed over large distances (-15 to 40 A) in DNA (see Figs 1,2), but no systematic distance dependence studies of the reaction rates have yet been forthcoming [4-111. If these reactions proceed by the conventional bridge-mediated electron-tunneling mechanism familiar in proteins, in which the uncertainty principle allows leakage of the electron from donor to acceptor guided by the intervening medium, the rate of electron transfer would be expected to drop exponentially with distance, and the drop-off factor is expected to be substantial [l&13].
Bridge‐mediated electronic interactions: Differences between Hamiltonian and Green function partitioning in a non‐orthogonal basis
An analysis of the partitioning (projection) technique is given with emphasis on non‐orthogonal basis sets. The general expression for the effective Hamiltonian obtained via Löwdin partitioning of the Schrödinger equation is discussed in the context of semi‐empirical theories and electron transfer matrix elements. Numerous pitfalls in calculations of matrix elements are pointed out. More importantly, it is shown that contrary to the case of an orthogonal basis, for a non‐orthogonal basis Löwdin partitioning of the Schrödinger equation and partitioning of the Green functionequation are not equivalent. The latter method provides a more general prescription for deriving effective Hamiltonians. Such Hamiltonians reproduce the full propagation in the partitioned subspace.
Acetylenyl-Linked, Porphyrin-Bridged, Donor−Acceptor Molecules:  A Theoretical Analysis of the Molecular First Hyperpolarizability in Highly Conjugated Push−Pull Chromophore Structures
We describe the theoretical basis for the exceptionally large molecular first hyperpolarizabilities inherent to (5,15-diethynylporphinato)metal-bridged donor−acceptor (D−A) molecules. β values relevant for hyper-Rayleigh experiments are calculated at 1.064 and 0.830 μm for a complex with such a structure, [5-((4‘-(dimethylamino)phenyl)ethynyl)-15-((4‘‘-nitrophenyl)ethynyl)-10,20-diphenylporphinato]zinc(II), and are 472 × 10-30 and 8152 × 10-30 cm5/esu, respectively. The values are 1 order of magnitude larger than that calculated for any other porphyrin bridged donor−acceptor chromophore studied to date. The considerably enhanced hyperpolarizability arises from the significant excited-state electronic asymmetry manifest in such structures (derived from the strong bridge-mediated D−A coupling enabled by the largely porphyrin-based excited state) and the large bridge-centered oscillator strength in this new class of D−bridge−A molecules. Our analysis of NLO properties (based upon INDO/SCI calculations within the sum over states formalism) shows a sensitivity to the degree of cumulenic character in the ground state. Calculations on structurally related multiporphyrin systems suggest candidate chromophores with further enhanced optical nonlinearities.
DNA Is Not a Molecular Wire:  Protein-like Electron-Transfer Predicted for an Extended π-Electron System
The earliest studies of electron-transfer proteins1 raised the question of whether or not π-electron residues might facilitate electron transport.2 Three recent long-range electron-transfer experiments utilizing DNA bridges revisit this provocative, yet unresolved, question.3,4,5 The distance dependence of electron transfer in DNA is not a matter of purely academic concern; it controls the mechanism of DNA damage and repair in cells and is being exploited in new molecular probes of DNA sequence. We present a theoretical analysis based upon very large scale self-consistent-field quantum calculation of all valence electrons (as many as ∼3300) in these three systems. This computation is the first performed on such large macromolecules and also the first to extract long-range electronic interactions at this level of theory. DNA electron transfer is found to be mediated by through-space interactions between the π-electron-containing base pairs, but the magnitude of the coupling facilitated by this channel drops rapidly with distance, as a consequence of the ∼3.4 Å noncovalent gap between base pairs. These predictions are in agreement with most of the experimental data. The rapid decay of electron-transfer rates with distance computed here suggests that biologically controlled DNA electron-transfer events, of importance in DNA repair,6 must function over relatively short range. Moreover, the predicted distance dependence of electron transfer in DNA is strikingly close to that found in proteins.
keynote speaking
Global Big Data Conference, Santa Clara, USA - January 18, 2015
Predictive modeling of DDoS attacks - Use for Big Data and Machine Learning
Global Big Data Conference, Tampa, USA - December 5-7, 2014
Data Science and Machine Learning Overview
Global Big Data Boot Camp, Irving, USA - November 21-23, 2014
Data Science and Machine Learning Overview
SPE - Data Driven Analytics Workshop, Galveston, USA - November 19, 2014
Leveraging Big Data Explosion in Oil and Gas Industry: A paradigm shift is needed to remain competitive
Data Analytics Week 2014, Las Vegas, USA - November 13, 2014
Moving Beyond Number Crunching
CIO Summit, Maimi, USA - November 11, 2014
Turning Big Data in Big Opportunity
SEG - Halliburton Landmark Forum, Denver, USA - October 26-29, 2014
Taking Exploration Geophysics to Next Level with Big Data
SEG - The Society of HPC Professionals, Denver, USA - October 26-29, 2014
Taking Exploration Geophysics to Next Level with Big Data
IEEE - Rockstars of Big Data, San Jose, USA - October 10, 2014
Deriving value from complex data
LIFE2014 - Landmark Innovation Forum , Paris, France - September 22-23, 2014
Prediction of Stuck-Pipe in Drilling - How can Data Science Help?
media coverage
Predictive Analytics Times - August 28 2015
Q: In your work with predictive analytics, what behavior do your models predict? A: In the upstream oil and gas industry, there is need for predictive analytics at various phases of oil well lifecycle. Predictive analytics plays a critical role in detecting events that could lead to increased cost of operations. Predictive analytics is also important to predict Black Swan events as well. Q: How does predictive analytics deliver value at your organization? What is one specific way in which it actively drives decisions? A: Upstream oil and gas is also referred as exploration and production (E&P) industry and focuses on health and safety across its operations. Predictive analytics plays a significant role in keeping this safety indicator score high for the organization and provides insights for intelligence based risk management. Q: Can you describe a successful result, such as the predictive lift of your model or the ROI of an analytics initiative? A: The operational cost depends on the drilling efficiency. Drilling efficiency depends on rate of penetration (ROP). ROP is defined as advancement per unit time while the drill bit is on bottom and drilling. ROP is considered one of the most critical performance qualifiers. ROP depends on many factors like the weight on bit, the rotating speed, the lithology, the formation drillability, etc. Predictive models that include these factors help provide lift in ROP which can also help improve significantly return on investment for drilling operations.
RigZone News- August 19, 2015
The three V words commonly used to describe Big Data – volume, velocity and variety – fail to fully describe the whole concept of Big Data, according to the chief data scientist of oilfield service firm Halliburton. Oil and gas companies are increasingly turning to Big Data as a means of better using structured and unstructured data generated by operations not only to enhance safety, efficiency and productivity, but to predict events before they happen. Instead of the three Vs, Big Data is more accurately described by the seven Vs, or the Seven Pillars of Big Data, said Dr. Satyam Priyadarshy, chief data scientist for Halliburton’s Landmark division, in an interview with Rigzone. Besides volume, variety and velocity, the other Vs, or pillars of Big Data, include: veracity, virtual, variability and value. The three Vs can actually be confusing terms, not actually describing the whole concept of Big Data. Volume and velocity can both be high or low, and variety now equals to all data, not just unstructured and structured data, said Priyadarshy. The fourth V, veracity – or correctness or accuracy of data or context of the data – is critical. “If someone changes data to conform to their ‘verifiable’ standards then that data is no longer raw,” Priyadarshy explained.
KDNuggets.com – The leading resources for Data Analytics - March 2015
Anmol Rajpurohit: Q1. What role does Analytics play at Halliburton? What are the typical problems that you work on? halliburtonDr. Satyam Priyadarshy: Halliburton, founded in 1919, is one of the world’s largest providers of products and services to the energy industry. The company serves the upstream oil and gas industry throughout the lifecycle of the reservoir. The life cycle phases include locating hydrocarbons, managing geological data, drilling and formation evaluation, well construction, well completion, and optimizing production through the life of the oil field. For each of these phases during the lifecycle of oil field, data and analytics play a significant role. However, based on which phase were talking about, some leverage a significant amount of knowledge creation through scientific and 1st principle models, augmented with data analytics, while others rely on data analytics more. The product service line that I am in is called Landmark. Landmark is a leader in providing Analytics platform for the upstream oil and gas industry that is used internally by Halliburton as well as by majority of the oil and gas companies. AR: Q2. How has the rapid advancement of emerging technology devices (such as sensors, IOTs, etc.) impacted Analytics in Oil and Gas industry? fast-analyticsSP: Yes, the deployment of IOTs, Sensors and M2M communication impacts the analytics in oil and gas in-dustry. For example, earlier it was fine to get the data from the remote sites and process in 24 hours or more, but as the new communication networks enable faster movement of data, the expectation for turnaround analytics based information reduces to near real-time.
E&P Magazine – Internet of things could transform the oil patch August 21, 2015
HOUSTON—When Moray Laing asks someone “What is the Internet of Things?” he gets a different answer every time. The IoT was described in a Fortune article as “the use of sensors and other Internet-connected devices to track and control physical objects.” Results from a Google search described it as a “scenario in which objects, animals or people are provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction.” Software firm SAS, where Laing is the executive lead for oil and gas, called it a “growing network of everyday objects—from industrial machines to consumer goods—that can share information and complete tasks while you are busy with other activities, like work, sleep or exercise.” The IoT consists of assets, communication networks between the assets and computing systems that use the data, which are gathered from sensors, and analyzed. Acquiring such data in real-time enables companies to analyze and adjust quickly. For the oil and gas sector, this could mean making drilling adjustments on the spot when activities downhole need to be fine-tuned. It could also mean collecting data from various sources, analyzing it and using that knowledge to grow production and revenue. “The whole concept is around enrichment of what we currently have,” Laing explained.
non-profit activities
Non profit activities
Elected Member, Phi Lambda and Upsilon, A National Honorary Society of Scientists Member, Board of Advisors, Information Technology Department, NVCC, Annandale, Virginia Member, Board of Advisors, ECPI University, Northern, Virginia President-Elect, Board Member and Charter Member, TiE (The Indus Entrepreneur), Washington, D.C. Chapter Chair, TiE The Young Entrepreneur Program for High-School Students Natonal Honor Society IUPAC ACS IEEE ACM
research papers
gcc bigdata, dubai 20151
The Big Data revolution is already changing the way that international governments and corporations think, plan – and analyse. And GCC nations are at a pivotal stage with regard to Big Data infrastructure build and business analytics investments. Governments across the Middle East are pioneering the use of business intelligence & technology to better serve citizens. The years 2015 – 2017 will see major decisions made on investments in Big Data concepts throughout public and private sector companies in the GCC, according to major IT research analysts.
speeches
GCC bigdata, Dubai 2015
The Big Data revolution is already changing the way that international governments and corporations think, plan – and analyse. And GCC nations are at a pivotal stage with regard to Big Data infrastructure build and business analytics investments. Governments across the Middle East are pioneering the use of business intelligence & technology to better serve citizens. The years 2015 – 2017 will see major decisions made on investments in Big Data concepts throughout public and private sector companies in the GCC, according to major IT research analysts. Leaders in the region are beginning to understand the potential that Big Data and Analytics can bring to their businesses. New insights from within the industry are now essential to drive organisational transformation for customers – handling volumes of readily available data in various velocity, variety and veracity capacities.
DNA: insulator or wire?
DNA-based electron transfer reactions are seen in processes such as biosynthesis and radiation damage/repair, but are poorly understood. What kinds of experiments might tell us how far and how fast electrons can travel in DNA? What does modern theory predict? Electron transfer reactions involving DNA are clearly important in nucleic acid biosynthesis, regulation, damage, and repair. Studies of electron transfer between species bound covalently and noncovalently to DNA have given provocative results, and are at a similar stage to that reached several years ago by the protein electron transfer community [l-3]. Electron transfer has been shown to proceed over large distances (-15 to 40 A) in DNA (see Figs 1,2), but no systematic distance dependence studies of the reaction rates have yet been forthcoming [4-111. If these reactions proceed by the conventional bridge-mediated electron-tunneling mechanism familiar in proteins, in which the uncertainty principle allows leakage of the electron from donor to acceptor guided by the intervening medium, the rate of electron transfer would be expected to drop exponentially with distance, and the drop-off factor is expected to be substantial [l&13].
Bridge‐mediated electronic interactions: Differences between Hamiltonian and Green function partitioning in a non‐orthogonal basis
An analysis of the partitioning (projection) technique is given with emphasis on non‐orthogonal basis sets. The general expression for the effective Hamiltonian obtained via Löwdin partitioning of the Schrödinger equation is discussed in the context of semi‐empirical theories and electron transfer matrix elements. Numerous pitfalls in calculations of matrix elements are pointed out. More importantly, it is shown that contrary to the case of an orthogonal basis, for a non‐orthogonal basis Löwdin partitioning of the Schrödinger equation and partitioning of the Green functionequation are not equivalent. The latter method provides a more general prescription for deriving effective Hamiltonians. Such Hamiltonians reproduce the full propagation in the partitioned subspace.
Acetylenyl-Linked, Porphyrin-Bridged, Donor−Acceptor Molecules:  A Theoretical Analysis of the Molecular First Hyperpolarizability in Highly Conjugated Push−Pull Chromophore Structures
We describe the theoretical basis for the exceptionally large molecular first hyperpolarizabilities inherent to (5,15-diethynylporphinato)metal-bridged donor−acceptor (D−A) molecules. β values relevant for hyper-Rayleigh experiments are calculated at 1.064 and 0.830 μm for a complex with such a structure, [5-((4‘-(dimethylamino)phenyl)ethynyl)-15-((4‘‘-nitrophenyl)ethynyl)-10,20-diphenylporphinato]zinc(II), and are 472 × 10-30 and 8152 × 10-30 cm5/esu, respectively. The values are 1 order of magnitude larger than that calculated for any other porphyrin bridged donor−acceptor chromophore studied to date. The considerably enhanced hyperpolarizability arises from the significant excited-state electronic asymmetry manifest in such structures (derived from the strong bridge-mediated D−A coupling enabled by the largely porphyrin-based excited state) and the large bridge-centered oscillator strength in this new class of D−bridge−A molecules. Our analysis of NLO properties (based upon INDO/SCI calculations within the sum over states formalism) shows a sensitivity to the degree of cumulenic character in the ground state. Calculations on structurally related multiporphyrin systems suggest candidate chromophores with further enhanced optical nonlinearities.
DNA Is Not a Molecular Wire:  Protein-like Electron-Transfer Predicted for an Extended π-Electron System
The earliest studies of electron-transfer proteins1 raised the question of whether or not π-electron residues might facilitate electron transport.2 Three recent long-range electron-transfer experiments utilizing DNA bridges revisit this provocative, yet unresolved, question.3,4,5 The distance dependence of electron transfer in DNA is not a matter of purely academic concern; it controls the mechanism of DNA damage and repair in cells and is being exploited in new molecular probes of DNA sequence. We present a theoretical analysis based upon very large scale self-consistent-field quantum calculation of all valence electrons (as many as ∼3300) in these three systems. This computation is the first performed on such large macromolecules and also the first to extract long-range electronic interactions at this level of theory. DNA electron transfer is found to be mediated by through-space interactions between the π-electron-containing base pairs, but the magnitude of the coupling facilitated by this channel drops rapidly with distance, as a consequence of the ∼3.4 Å noncovalent gap between base pairs. These predictions are in agreement with most of the experimental data. The rapid decay of electron-transfer rates with distance computed here suggests that biologically controlled DNA electron-transfer events, of importance in DNA repair,6 must function over relatively short range. Moreover, the predicted distance dependence of electron transfer in DNA is strikingly close to that found in proteins.
Global Big Data Conference, Santa Clara, USA - January 18, 2015
Predictive modeling of DDoS attacks - Use for Big Data and Machine Learning
Global Big Data Conference, Tampa, USA - December 5-7, 2014
Data Science and Machine Learning Overview
Global Big Data Boot Camp, Irving, USA - November 21-23, 2014
Data Science and Machine Learning Overview
SPE - Data Driven Analytics Workshop, Galveston, USA - November 19, 2014
Leveraging Big Data Explosion in Oil and Gas Industry: A paradigm shift is needed to remain competitive
Data Analytics Week 2014, Las Vegas, USA - November 13, 2014
Moving Beyond Number Crunching
CIO Summit, Maimi, USA - November 11, 2014
Turning Big Data in Big Opportunity
SEG - Halliburton Landmark Forum, Denver, USA - October 26-29, 2014
Taking Exploration Geophysics to Next Level with Big Data
SEG - The Society of HPC Professionals, Denver, USA - October 26-29, 2014
Taking Exploration Geophysics to Next Level with Big Data
IEEE - Rockstars of Big Data, San Jose, USA - October 10, 2014
Deriving value from complex data
LIFE2014 - Landmark Innovation Forum , Paris, France - September 22-23, 2014
Prediction of Stuck-Pipe in Drilling - How can Data Science Help?
Predictive Analytics Times - August 28 2015
Q: In your work with predictive analytics, what behavior do your models predict? A: In the upstream oil and gas industry, there is need for predictive analytics at various phases of oil well lifecycle. Predictive analytics plays a critical role in detecting events that could lead to increased cost of operations. Predictive analytics is also important to predict Black Swan events as well. Q: How does predictive analytics deliver value at your organization? What is one specific way in which it actively drives decisions? A: Upstream oil and gas is also referred as exploration and production (E&P) industry and focuses on health and safety across its operations. Predictive analytics plays a significant role in keeping this safety indicator score high for the organization and provides insights for intelligence based risk management. Q: Can you describe a successful result, such as the predictive lift of your model or the ROI of an analytics initiative? A: The operational cost depends on the drilling efficiency. Drilling efficiency depends on rate of penetration (ROP). ROP is defined as advancement per unit time while the drill bit is on bottom and drilling. ROP is considered one of the most critical performance qualifiers. ROP depends on many factors like the weight on bit, the rotating speed, the lithology, the formation drillability, etc. Predictive models that include these factors help provide lift in ROP which can also help improve significantly return on investment for drilling operations.
RigZone News- August 19, 2015
The three V words commonly used to describe Big Data – volume, velocity and variety – fail to fully describe the whole concept of Big Data, according to the chief data scientist of oilfield service firm Halliburton. Oil and gas companies are increasingly turning to Big Data as a means of better using structured and unstructured data generated by operations not only to enhance safety, efficiency and productivity, but to predict events before they happen. Instead of the three Vs, Big Data is more accurately described by the seven Vs, or the Seven Pillars of Big Data, said Dr. Satyam Priyadarshy, chief data scientist for Halliburton’s Landmark division, in an interview with Rigzone. Besides volume, variety and velocity, the other Vs, or pillars of Big Data, include: veracity, virtual, variability and value. The three Vs can actually be confusing terms, not actually describing the whole concept of Big Data. Volume and velocity can both be high or low, and variety now equals to all data, not just unstructured and structured data, said Priyadarshy. The fourth V, veracity – or correctness or accuracy of data or context of the data – is critical. “If someone changes data to conform to their ‘verifiable’ standards then that data is no longer raw,” Priyadarshy explained.
KDNuggets.com – The leading resources for Data Analytics - March 2015
Anmol Rajpurohit: Q1. What role does Analytics play at Halliburton? What are the typical problems that you work on? halliburtonDr. Satyam Priyadarshy: Halliburton, founded in 1919, is one of the world’s largest providers of products and services to the energy industry. The company serves the upstream oil and gas industry throughout the lifecycle of the reservoir. The life cycle phases include locating hydrocarbons, managing geological data, drilling and formation evaluation, well construction, well completion, and optimizing production through the life of the oil field. For each of these phases during the lifecycle of oil field, data and analytics play a significant role. However, based on which phase were talking about, some leverage a significant amount of knowledge creation through scientific and 1st principle models, augmented with data analytics, while others rely on data analytics more. The product service line that I am in is called Landmark. Landmark is a leader in providing Analytics platform for the upstream oil and gas industry that is used internally by Halliburton as well as by majority of the oil and gas companies. AR: Q2. How has the rapid advancement of emerging technology devices (such as sensors, IOTs, etc.) impacted Analytics in Oil and Gas industry? fast-analyticsSP: Yes, the deployment of IOTs, Sensors and M2M communication impacts the analytics in oil and gas in-dustry. For example, earlier it was fine to get the data from the remote sites and process in 24 hours or more, but as the new communication networks enable faster movement of data, the expectation for turnaround analytics based information reduces to near real-time.
E&P Magazine – Internet of things could transform the oil patch August 21, 2015
HOUSTON—When Moray Laing asks someone “What is the Internet of Things?” he gets a different answer every time. The IoT was described in a Fortune article as “the use of sensors and other Internet-connected devices to track and control physical objects.” Results from a Google search described it as a “scenario in which objects, animals or people are provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction.” Software firm SAS, where Laing is the executive lead for oil and gas, called it a “growing network of everyday objects—from industrial machines to consumer goods—that can share information and complete tasks while you are busy with other activities, like work, sleep or exercise.” The IoT consists of assets, communication networks between the assets and computing systems that use the data, which are gathered from sensors, and analyzed. Acquiring such data in real-time enables companies to analyze and adjust quickly. For the oil and gas sector, this could mean making drilling adjustments on the spot when activities downhole need to be fine-tuned. It could also mean collecting data from various sources, analyzing it and using that knowledge to grow production and revenue. “The whole concept is around enrichment of what we currently have,” Laing explained.
Non profit activities
Elected Member, Phi Lambda and Upsilon, A National Honorary Society of Scientists Member, Board of Advisors, Information Technology Department, NVCC, Annandale, Virginia Member, Board of Advisors, ECPI University, Northern, Virginia President-Elect, Board Member and Charter Member, TiE (The Indus Entrepreneur), Washington, D.C. Chapter Chair, TiE The Young Entrepreneur Program for High-School Students Natonal Honor Society IUPAC ACS IEEE ACM
gcc bigdata, dubai 20151
The Big Data revolution is already changing the way that international governments and corporations think, plan – and analyse. And GCC nations are at a pivotal stage with regard to Big Data infrastructure build and business analytics investments. Governments across the Middle East are pioneering the use of business intelligence & technology to better serve citizens. The years 2015 – 2017 will see major decisions made on investments in Big Data concepts throughout public and private sector companies in the GCC, according to major IT research analysts.
GCC bigdata, Dubai 2015
The Big Data revolution is already changing the way that international governments and corporations think, plan – and analyse. And GCC nations are at a pivotal stage with regard to Big Data infrastructure build and business analytics investments. Governments across the Middle East are pioneering the use of business intelligence & technology to better serve citizens. The years 2015 – 2017 will see major decisions made on investments in Big Data concepts throughout public and private sector companies in the GCC, according to major IT research analysts. Leaders in the region are beginning to understand the potential that Big Data and Analytics can bring to their businesses. New insights from within the industry are now essential to drive organisational transformation for customers – handling volumes of readily available data in various velocity, variety and veracity capacities.
March 29, 2016
The 7 pillars of Big Data.. My interview published in Magazine Petroleum Review, January 2015. Full article is availabe from Energy Institute Site. Full Article   7 Pillars of Big…
March 29, 2016
There are quite a few articles, reports and presentations which will present Big Data as an hype or fad. In my humble opinion, such comments only shows that folks do…
March 19th, 2016
Big Analytics is being talked about a lot these days. I briefly present here a timeline of various important events that enables us to do Big Analytics.
March 19th, 2016
As mentioned in previous post, 7 V’s are important for describing Big Data. Here is simple graphics that I came up with to show the 7V’s. Big Data and 7...
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publications
DNA Conductance
S. Priyadarshy.
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DNA Conductance
A Basic Review. Synthesis and Reactivity in Inorganic, Metal-Organic, and Nano-metal chemistry, 37(5), 353, 2007
S. Priyadarshy.
Flexible Analysis Powerhouse
Science 297, 2079 (2002)
K. Pallavi and S. Priyadarshy. Discusses Axum 7.0, data-analysis and statistical analysis package.
Drug Discoverer
Science 297, 1199 (2002)
S. Priyadarshy and P. Simakov. Reviews Catalyst 4.7 from Accelrys -an integrated environment for visualizing, testing hypothesis and performing database analyses related to drug discovery.
Chemical Design and Modeling
Science 296, 2419 (2002)
S. Priyadarshy. Reviews HyperChem an easy to use, intuitive molecular modeling package.
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