International Journal of Computer Science & Business Informatics Designing Condition-based Maintenance Management Systems for High-Speed Fleet
Abstract: Advancement in the big-data technologies in combination with machine-to-machine (M2M) interconnectivity and predictive analytics is creating new possibilities for real-time analysis of machine components for identifying and avoiding breakdowns in the early stages ahead of time. Designing such a condition-based maintenance system for high-speed fleet requires special attention to the design methodologies used in collecting the operating requirements from the users and translating them into big-data parallel architectures that are capable of exhibiting fault-tolerant behavior and load-balancing possibilities to sustain the real-time data processing demands. This paper discusses the M2M approach for the big-data condition-based maintenance system and the requirement specification steps involved in building such a system, along with the cost-savings benefited from the system.
Citation: GK Palem. Designing Condition-based Maintenance Management Systems for High-Speed Fleet. International Journal of Computer Science & Business Informatics. Vol. 17, No. 1, pp. 28-40, Jun 2017. (PDF )
Technology Innovation Management Review Formulating an Executive Strategy for Big Data Analytics
Abstract: The recent surge in big data technologies has left many executives, both of well-established organizations and emerging startups, wondering how best to harness big data. In particular, the analytics aspect of big data is enticing for both information technology (IT) service providers and non-IT firms because of its potential for high returns on investment, which have been heavily publicized, if not clearly demonstrated, by multiple whitepapers, webinars, and research surveys. Although executives may clearly perceive the benefits of big data analytics to their organizations, the path to the goal is not as clear or easy as it looks. And, it is not just the established organizations that have this challenge; even startups trying to take advantage of this big data analytics opportunity are facing the same problem of lack of clarity on what to do or how to formulate an executive strategy. This article is primarily for executives who are looking for help in formulating a strategy for achieving success with big data analytics in their operations. It provides guidelines to them plan an organization's short-term and long-term goals, and presents a strategy tool, known as the delta model, to develop a customer-centric approach to success with big data analytics.
Citation: GK Palem. Formulating an Executive Strategy for Big Data Analytics. Technology Innovation Management Review. March 2014: 25–34. (PDF )
Big-data Technical Report Condition-based Maintenance for High-speed Fleet
Abstract: Maintenance accounts for approx. 30% of the lifecyle costs of a high-speed train, making it the largest rolling stock operating cost factor besides energy. Predictive maintenance, also known as Condition Based Maintenance (CBM) aims to reduce these unnecessary costs by basing the maintenance need on the actual condition of themachine rather than on preset schedules or assumptions.
Citation: GK Palem. Condition-based Maintenance for High-speed Fleet. Big-data Technical Report, Sep 2013. (PDF )
Health Informatics International Journal Medicare Healthcare Charge Disparity Analysis
Abstract: Transparency in administration and effective corporate governance leads to huge volumes of public data that upon processing with analytical procedures yield meaningful insights. A case in point being the recent public release of United States Medicare charges for healthcare system.The data contains in-patient provider charges, number of discharges per each hospital for every DRG and the respective reimbursements. This paper presents our analysis on the intricacies involved in analyzing such public data, along with the results we obtained in the process. We also present the disease classification system that was used to identify the culprit hospitals causing the disparity.
Citation: GK Palem. Medicare Healthcare Charge Disparity Analysis. Health Informatics International Journal, 2(3):9–15, August 2013. (PDF )
International Journal of Mobile Network Communications & Telematics Condition-Based Maintenance using Sensor Arrays and Telematics
Abstract: Emergence of uniquely addressable embeddable devices has raised bar on Telematics capabilities. Sensor based Telematics technologies generate volumes of data that are orders of magnitude larger than what operators have dealt with previously. Real-time big data architectures enable real-time control and monitoring of data to detect anomalies and take preventive action. Condition-based-maintenance, usage-based-insurance, smart metering and demand-based load generation are some of the predictive analytics use cases for Telematics with real-time data streaming. This paper presents indepth analysis of condition-based maintenance using real-time sensor monitoring, Telematics and predictive data analytics.
Citation: GK Palem. Condition-Based Maintenance using Sensor Arrays and Telematics. International Journal of Mobile Network Communications & Telematics, 3(3):19–28, June 2013. (PDF )
Predictive Analytics Technical Report The Practice of Predictive Analytics in Healthcare
Abstract: Problems such as inaccurate diagnoses and poor drug-adherence pose challenges to individual health and safety. These challenges are now being alleviated with big data analytics using personalized drug regimes, epidemic outbreak detection, follow-up alerts and real-time diagnosis monitoring. This paper describes in detail how predictive analytics is helping healthcare industry with possibilities such as clinical decision support systems, medical text analysis and Electronic Health Records (EHR).
Citation: GK Palem. The Practice of Predictive Analytics in Healthcare. Predictive Analytics white-paper, April 2013. (PDF )
Big-data Technical Report M2M Telematics & Predictive Analytics
Abstract: Regulations such as eCall are giving rise to significant demand for Telematics, enabling several additional services to be layered on the basic M2M communications. Some of the potential applications on the rise are: Remote Diagnostics, Condition based Predictive Maintenace, Usage based Insurance Services, Eco-driving, Tele-Medecine and Smart grid. It is the combination of Telematics with predictive analytics on real-time Big Data that makes many of these innovations possible, and this paper presents indepth study of these strategic offerings and how to benefit from them.
Citation: GK Palem. M2M Telematics & Predictive Analytics. Big data Technical Report, Feb 2013. (PDF )
The ITIL Experience Creating Distributed Enterprise CRM System for Service Management
Abstract: Continual Service Improvement (CSI) in ITSM is a self-invalidating activity that sometimes triggers changes right at the core of operations. It might be the way daily operations are performed by the frontline engineers or the way strategic decisions are made by the business owners, where ever the change be, without a right balance between the need for improvement and the urge to stick to the existing system, the results could be catastrophic. And finding the right balance, it comes only with experience. This publication presents a detailed case study of one of my recent CSI implementations.
Citation: GK Palem. Creating Distributed Enterprise CRM System for Service Management. In Ivanka Menken, editor, The ITIL Experience: 67 Real Life Remarkable ITIL Experiences . Emereo Pty Ltd, 2009. (HTML )
Game Programming Gems 6 Sequence Indexing for Game Development
Abstract: Sequence indexing is the mathematical art of manipulating computational objects. It tries to answer one simple question: Given a collection of objects, how do we create, access, and identify various groups of objects drawn from that collection? Most games consist of large collections of objects, and mathematically, we represent these collections of objects as a sequence. This gem presents the mathematical formulas for indexing and deindexing game objects as few well-known sequences, such as range sequences, permutation sequences, and combinatorial sequences, offering guidelines on how to use them in game environments for various tasks, ranging from simulating deterministic randomness to serializing game objects.
Citation: GK Palem. Sequence Indexing for Game Development. In Michael Dickheiser, editor, Game Programming Gems 6 , pages 161–174. Charles River Media, 2006
Proc. of Approaches and Applications of Inductive Programming Data-dependencies and Learning in Artificial Systems
Abstract: Data-dependencies play an important role in the performance of machine learning algorithms. This paper analyzes the concepts of data dependencies in the context of artificial learning systems. When a problem and its solution are viewed as points in a system configuration, variations in the problem configurations can be used to study the variations in the solution configurations and vice versa. These variations could be used to infer solutions to unknown instances of problems based on the solutions to known instances, thus reducing the problem of learning to that of identifying the relations among problems and their solutions. We use this concept in constructing a formal knowledge conservation framework for a learning mechanism based on the relations among data attributes.
Citation: GK Palem. Data-dependencies and Learning in Artificial Systems. In Proc. of Approaches and Applications of Inductive Programming, page 69, 2005. (PDF )
Proc. of the International Conference on Systemic, Cybernetics and Informatics On Solving the System
Abstract: Mathematically a system is said to be solved if its future states can be predicted from the information provided by the present and past state history. In this paper we present a way of solving artificial life systems using the principles of state-machines. We present the view of manipulating the artificial systems considering them as being embedded in external program entities. Further, we discuss the technique of using algorithmic transformations to understand the behavioral complexity of virtual organisms. Finally, we relate the complexity of virtual systems with the algorithmic complexity and establish that open-ended evolution requires programs with ever increasing algorithmic complexity.
Citation: GK Palem. On Solving the System. In Proc. of the International Conference on Systemic, Cybernetics and Informatics, 2005
Technical Report On Ontic Oracles and Epistemic Artificial Life Systems
Abstract: Oracles are hypothetical machines that can compute any function in unit time. They are representatives of the ontic truth that is independent of any theory or model. Artificial life systems are synthetic models that try to capture the ontic truth through epistemic inferences. Their descriptions are epistemic perceptions of the observer. Understanding the distinctions between these ontic-epistemic counterparts is vital for building successful models of nature. Towards this extent this paper formalizes the notions of oracles and o-machines and establishes their role in solving the decision problem for artificial life systems. It further examines the converse of Church-Turing thesis that holds the key to the possibility of practical realization of those hypothetical machines.
Citation: GK Palem. On Ontic Oracles and Artificial Life Systems. Technical Report, 2005. (PDF )