Columbia, MD, United States
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Branch J.W.,IBM | Giannella C.,Mitre Corporation | Szymanski B.,Rensselaer Polytechnic Institute | Wolff R.,Haifa University | And 2 more authors.
Knowledge and Information Systems | Year: 2013

To address the problem of unsupervised outlier detection in wireless sensor networks, we develop an approach that (1) is flexible with respect to the outlier definition, (2) computes the result in-network to reduce both bandwidth and energy consumption, (3) uses only single-hop communication, thus permitting very simple node failure detection and message reliability assurance mechanisms (e. g., carrier-sense), and (4) seamlessly accommodates dynamic updates to data. We examine performance by simulation, using real sensor data streams. Our results demonstrate that our approach is accurate and imposes reasonable communication and power consumption demands. © 2012 Springer-Verlag London Limited.


Mallik R.,University of Maryland Baltimore County | Kargupta H.,University of Maryland Baltimore County | Kargupta H.,Agnik LLC
Proceedings of the 2011 Conference on Intelligent Data Understanding, CIDU 2011 | Year: 2011

Energy production, distribution, and consumption play a critical role in the sustain-ability of the planet and its natural resources. Electric power systems have been going through major changes that are aimed to make the energy infrastructure "smarter", scalable, and more efficient. These new generation of smart energy grids need novel computational algorithms for supporting generation of power from wide range of sources, efficient energy distribution, and sustainable consumption. This paper argues that a fundamentally distributed approach with more local flexibility is a lot more sustainable methodology compared to the traditional centralized frameworks for analyzing and processing data. It considers the problem of predicting power generation and consumption trends over a distributed smart grid. Since power generation from solar, wind, geothermal and other renewable sources are likely to be part of many households in near future, both power generation and consumption data will be generated over a wide area network. Moreover, a good part of the communication links between the household data sources and the central server are likely to be over the wireless networks with low bandwidth and high data-plan cost. Analyzing such data (some of it privacy sensitive) in a centralized is not scalable, sometimes not privacy-preserving, and often not practical because of cost-sensitivity of the applications. This paper presents a more sustainable distributed asynchronous algorithm for constructing energy demand prediction models in a smart grid by multivariate linear regression. The paper offers the algorithm, analysis, and experimental results.


Das K.,NASA | Bhaduri K.,Critical Technologies Inc | Kargupta H.,University of Maryland Baltimore County | Kargupta H.,Agnik LLC
Peer-to-Peer Networking and Applications | Year: 2011

This paper proposes a scalable, local privacy-preserving algorithm for distributed Peer-to-Peer (P2P) data aggregation useful for many advanced data mining/analysis tasks such as average/sum computation, decision tree induction, feature selection, and more. Unlike most multi-party privacy-preserving data mining algorithms, this approach works in an asynchronous manner through local interactions and it is highly scalable. It particularly deals with the distributed computation of the sum of a set of numbers stored at different peers in a P2P network in the context of a P2P web mining application. The proposed optimization-based privacy-preserving technique for computing the sum allows different peers to specify different privacy requirements without having to adhere to a global set of parameters for the chosen privacy model. Since distributed sum computation is a frequently used primitive, the proposed approach is likely to have significant impact on many data mining tasks such as multi-party privacy-preserving clustering, frequent itemset mining, and statistical aggregate computation. © 2010 Springer Science+Business Media, LLC.


Grant
Agency: Department of Transportation | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 998.88K | Year: 2016

This proposal suggests research on developing the next generation of consumer experience for Usage Based Insurance (UBI) in an increasingly socially connected world while addressing the need to protect privacy. It also proposes research on methodologies to address this from the perspective of insurance actuarial science. It will enhance Agnik’s current consumer and UBI products through the following innovations: a) Collective Incentives and Game Theory: Explore the problem of risk modeling and underwriting from a social perspective using a game theoretic analytical framework in order to design loyalty programs for drivers. b) Pattern Preserving Encryption: Develop privacy-protecting data analytics algorithms for blending “pattern-preserving cryptography” with data analytics so that driver data can be analyzed without having to access raw privacy-sensitive location or driving data. c) Reducing Cost and Enhancing the UBI Experience: Analytical results will be integrated with Agnik’s smartphone-based and OBD-II dongle-based solutions with various value-added services. Agnik’s consumer market products are currently sold by companies like Walmart, Amazon, VOXX, and AT&T. The proposed research will be performed by the Agnik team in collaboration with several insurance carriers such as American Family, General, Plymouth Rock in addition to Agnik’s distribution partners such as VOXX and Sprint (supporting letters enclosed).


The present invention relates to a system and method for performing vehicle onboard analysis on the data associated with the vehicle and implementing a cloud-based distributed data stream mining algorithm for detecting patterns from vehicle diagnostic and correlating the pattern with the contextual data. The system applies the distributed data mining algorithms for mining the results of the vehicle onboard analytics sent over the wireless network to the server and correlates the analyzed data with the contextual data of the vehicle. The system extracts performance patterns from data, builds predictive models from vehicle diagnostic, and correlates the predicted model with the business process using state of the art link analysis techniques.


Das K.,NASA | Bhaduri K.,Critical Technologies Inc | Kargupta H.,University of Maryland Baltimore County | Kargupta H.,Agnik LLC
Knowledge and Information Systems | Year: 2010

In this paper we develop a local distributed privacy preserving algorithm for feature selection in a large peer-to-peer environment. Feature selection is often used in machine learning for data compaction and efficient learning by eliminating the curse of dimensionality. There exist many solutions for feature selection when the data are located at a central location. However, it becomes extremely challenging to perform the same when the data are distributed across a large number of peers or machines. Centralizing the entire dataset or portions of it can be very costly and impractical because of the large number of data sources, the asynchronous nature of the peer-to-peer networks, dynamic nature of the data/network, and privacy concerns. The solution proposed in this paper allows us to perform feature selection in an asynchronous fashion with a low communication overhead where each peer can specify its own privacy constraints. The algorithm works based on local interactions among participating nodes. We present results on real-world dataset in order to test the performance of the proposed algorithm. © 2009 Springer-Verlag London Limited.


Agnik LLC | Entity website


Agnik LLC | Entity website

Career with Agnik Located near Baltimore, our offices are about 15 miles from BWI airport and from Baltimore's main attractions and evening entertainment venues. Agnik employment package includes competitive salary, health, dental, and prescription benefits, a traditional 401(K) plan, profit sharing plan, paid vacation, and sick leave ...


Agnik LLC | Entity website

Key Advantages For Your Business: Easy integration GPS-free automatic detection of vehicle movement No user interaction necessary Automated Driver-Passenger detection Automated transportation mode detection Accurate mileage detection and trip statistics Route driven Timestamps for the start and end of trips Event notification Driver Score and Driving Behavior Analysis Speeding Harsh Braking Rapid Acceleration High-G Events Minimal battery consumption Minimal data requirements Extended connected life analytics beyond driving Recall Notices Alerts your users if their vehicle has a recall Wide range of insurance analytics through Agniks MineDrive Product Potential for an additional revenue stream Maintenance Reminders Users will receive notifications when the vehicle needs to be serviced as per factory specifications or has a service alert. For Your Users: Easy to use, no plug-in device needed No need to start/stop trips ...


Agnik LLC | Entity website

Latest News March 31, 2016: Agnik Achieves Logistics Tech Outlook's Ranking for Top 10 Fleet Management Solution Providers 2016. Read More ...

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