Welcome to TiddlyWiki created by Jeremy Ruston, Copyright © 2007 UnaMesa Association
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To get started with this blank TiddlyWiki, you'll need to modify the following tiddlers:
* SiteTitle & SiteSubtitle: The title and subtitle of the site, as shown above (after saving, they will also appear in the browser title bar)
* MainMenu: The menu (usually on the left)
* DefaultTiddlers: Contains the names of the tiddlers that you want to appear when the TiddlyWiki is opened
You'll also need to enter your username for signing your edits: <<option txtUserName>>
These InterfaceOptions for customising TiddlyWiki are saved in your browser
Your username for signing your edits. Write it as a WikiWord (eg JoeBloggs)
<<option txtUserName>>
<<option chkSaveBackups>> SaveBackups
<<option chkAutoSave>> AutoSave
<<option chkRegExpSearch>> RegExpSearch
<<option chkCaseSensitiveSearch>> CaseSensitiveSearch
<<option chkAnimate>> EnableAnimations
----
Also see AdvancedOptions
''
*IEEE SCC 2007 Best Student Paper Award
*TAC Competition 2006 Best New Agent
*Robo Cup 2005, OSAKA Japan, 3rd Place - Robo Rescue Agent Simulation
''
''
International Institute of Information Technology
Gachibowli
Hyderabad 500032
INDIA
Phone: +91 40 2300 1967/1969 -140/ 202 (home)
Fax: +91 40 2300 1413
Email: kamal@iiit.ac.in, kamal@acm.org
''
[[Research Interests]]
[[Recent Work]]
''
!Graduated Students
**Chi-wai Fung (PhD)
**Dickson Chiu (PhD)
**Eleanna Kafeza (PhD)
**Patric C. K. Hung (PhD)
!PhD Students
**Satyanarayana Valluri - Query Optimization
**Soujanya Vadapalli - Data Clustering
**Lini Thomas - Graph Mining
**Ritesh Tiwari - Information Systems Security
**Ragunath Reddy
**P Radhakrishna - Electronic Contracts
**Anuradha Pentareddy
!MS by Research Students
**Vidit Bansal - Multi Agent Systems Simulation
**Phanikumar Bhamidipati - Text Mining
**Anushri Khandekar - Conceptual Modeling & Electronic Contracts
**Meghna Lowalekar - Information Systems Security
**P. Ravali - Text Mining
**Rahul Sarika - Robocup - Scalability
**Sadhu Harith Siddartha - Robocup - Scalability
**Swati Sharma - Data Mining Time Series
**Hareesha Tamatam - Data Mining
**Raghavendran B
**Munkarna Biradar
**Koteswara Rao Kolli
**Saurabh Kumar
**Sri Lakshmi Nunna
**Raghu Chandra P
**Padmini Velidi
**Anupama Gali
**N. Harshita
**Mohd Asrar Ahmed
''
''
[[Research Interests]]
[[Recent Work]]
[[Select Publications]]
[[Achievements]]
[[Guidance]]
[[Teaching]]
[[Profiles]]
[[Professional Service]]
[[Contact]]
''
COMAD 2008 (PC); ER 2008 (PC); AAMAS 2008 (Senior PC), 2007 (PC); DASFAA 2008 (PC); ICDM 2007; RoboCup Symposium 2008, 2007 (PC); ER 2007 (PC);PRIMA 2007; WITS 2008, 2007 (PC); ICDCIT 2008, 2007 (PC); ICISS 2007 (PC)
''
*[[DBLP|http://www.informatik.uni-trier.de/~ley/db/indices/a-tree/k/Karlapalem:Kamalakar.html]]
*[[Google Scholar|http://scholar.google.co.in/scholar?hl=en&lr=&q=author%3Ak-karlapalem&btnG=Search]]
*[[ACM Author Profile|http://portal.acm.org/author_page.cfm?id=81100332715&coll=GUIDE&dl=GUIDE&trk=0]]
*[[Linked In|http://www.linkedin.com/pub/0/5B7/7B5]]
''
!2008
#M. Lowalekar, R. Tiwari, K. Karlapalem,Security Policy Satisfiability and Failure Resilience in Workflows, The Future of Identity in the Information Society - Challenges for Privacy and Security FIDIS/IFIP Internet Security & Privacy Summer School, 2008
#Madhu Smita, A. Mitra, H. Singh, K. Karlapalem, A Real Valued Genetic Algorithm for Generating Native Like Structure of Small Globular Protein, in IEEE EMBC 2008
#P. Radhakrishna, K. Karlapalem, Electronic Contracts, to appear as Spotlight Article in IEEE Internet Computing, July/August 2008
#R. Sarika, H. Siddhartha, K. Karlapalem, Database Driven RoboRescue Server, Robocup Symposium, 2008
#R. Monga, K. Karlapalem, MASFMMS: Multi Agent Systems Framawork for Malware Modeling and Simulation, in 9th MABS workshop co-located with AAMAS 2008
#K. Vidyasankar, P. Radhakrishna, K. Karlapalem, Study of Execution Centric Payment Issues in E-contracts, in SCC 2008
#K.Karlapalem, P. Radhakrishna, Electronic Contracts, ''Tutorial'' at ''WWW'' 2008
!2007
#Madhusmita, H. Singh, A. Mitra, K Karlapalem, Structure Simulation of Pentapeptide with Real valued Genetic Algorithm, in Bioconvene 2007
#L. Thomas, U. Singh, A. Mitra, K. Karlapalem, RNAMARGIN: A Graph Mining Approach for Automated Exploration of Common Substructures in RNA molecules, in Bioconvene 2007
#K. Vidyasankar, P. Radhakrishna, K. Karlapalem, A Multi Level Model for Activity Commitments in E-contracts, in Coop-IS 2007
#P. Radhakrishna and K. Karlapalem,Active Meta Modeling Support for Evolving E-contracts, in ER 2007
#A. Khandekar, P. Radhakrishna, K. Karlapalem, A Methodology and Toolkit for Deploying Contract Documents as E-contracts in ER 2007
#K. Karlapalem and P. Radhakrishna, State of the Art in Modeling and Deployment of E-Contracts, tutorial in IEEE ICWS/SCC 2007
#R. K. Tiwari, V. Dwivedi, K. Karlapalem, SLA Driven Process Security through Monitored E-contracts, IEEE SCC 2007
#V. Bansal, R. Kota and K. Karlapalem, Systems Issues in Multi-Agent Simulation of Large Crowds, in MABS 2007, held in conjunction with AAMAS 2007
#I. V. A. Rao, M. Jain and K. Karlapalem, Towards Simulating Billions of Agents in Thousands of Seconds, in AAMAS 2007
!2006
#L. Thomas, S. Valluri and K. Karlapalem, MARGIN: Maximal Frequent Subgraph Mining, in ICDM 2006
# S. Vadapalli, S. Valluri and K. Karlapalem, A Simple Yet Effective Data Clustering Algorithm, in ICDM 2006
''
*Data Mining and Data Warehousing - Data Clustering, Text Mining, Data Cubes, Materialized Views
*Database Systems - Query Optimization, Distributed Database Systems
*Electronic Contracts and Workflow Systems
*Conceptual Modeling
*Multi Agent Systems - Simulation and Modeling, Scalabilty, Robocup and Trading Agent competitions
*Information Systems Security
''
!Multi Agent Systems Simulation
*Praveen Paruchuri, Alok Reddy Pullalarevu, Kamalakar Karlapalem: Multi agent simulation of unorganized traffic. AAMAS 2002: 176-183
**We designed and implemented a road traffic simulator using multi agent systems, catering to the Indian scenario. We modeled the behavior of a human driver, and evaluated the traffic scenarios that occur, like gridlock and dominance affect. We showed that the effectiveness of traffic lights in improving the traffic throughput.
*Vidit Bansal, Ramachandra Kota and Kamalakar Karlapalem: System Issues in Multi-Agent Simulation of Large Crowds. MABS 2007
**Multi agent simulation is slow because of the high computation load of agent processing, and the delays due to agent communication. We showed that by using a back-end database system, we can simulate a large number of agents and illustrated it by simulating large crowd evacuation scenarios.
*I. V. Aprameya Rao, Manish Jain, Kamalakar Karlapalem: Towards simulating billions of agents in thousands of seconds. AAMAS 2007: 143
**We built a toolkit, DMASF, using Python to have a scalable and distributed simulation platform. We tested this simulator to simulate upto hundreds of millions of agents doing routine tasks. We showed that we can simulate communication-heavy agent environments with hundreds of thousands of agents within a reasonable time.
*Rohan Monga, Kamalakar Karlapalem: MASFMMS: Multi Agent Systems Framework for Malware Modeling and Simulation, in MABS 2008
**Modeling and simulating the spread of a computer virus/malware is tricky. We implemented a multi-agent system simulation to simulate a network with 10000-20000 computers and studied the effect of the spread of the computer virus. We also observed the spread of the anti-virus patches in combating the infection in this simulation.
*Rahul Sarika, Harith Siddhartha, Kamalakar Karlapalem: Database Driven RoboCup Rescue Server. To appear in Robocup Symposium 2008
**We enhanced the current main-memory dependent RoboCup Rescue simulator to work off a database management system. This enhanced the simulator to support three times more agents than it currently does. Efforts are under way to implement a distributed RoboCup Rescue simulator that can simulate 100000 civilians and hundreds of ambulances, police cars, and fire engines.
!Electronic Contracts
*Kamalakar Karlapalem, Ajay R. Dani, P. Radha Krishna: A Framework for Modeling Electronic Contracts. ER 2001: 193-207
**We extended the ER model to support electronic contracts. We developed a meta-model driven, template framework for designing a generic conceptual model for electronic contracts. This model can be customized for various domains.
*P. Radha Krishna, Kamalakar Karlapalem, Dickson K. W. Chiu: An EREC framework for e-contract modeling, enactment and monitoring. Data Knowl. Eng. 51(1): 31-58 (2004)
**We extended the core model to support activity commitment and dependencies among the e-contract clauses and activities. We presented a methodology to map e-contract activities to workflows along with an implementation architecture.
*K. Vidyasankar, P. Radha Krishna, Kamalakar Karlapalem: A Multi-level Model for Activity Commitments in E-contracts. OTM Conferences (1) 2007: 300-317
**The activities of e-contracts can have many kinds of commitment scenarios. The successful execution of activities will determine the satisfaction of clauses, which in turn, determines the successful completion of the e-contract. In this paper, we introduced the issues related to e-contract commitment.
*K. Vidyasankar, P. Radha Krishna, Kamalakar Karlapalem: Study of Execution Centric Payment Issues in E-contracts. to appear in SCC 2008
**Electronic contracts have payments to be made at various stages. In this paper, we studied the interdependencies between payments and commitment issues of electronic contracts. Any payment system for electronic contracts needs to address these issues.
*Kamalakar Karlapalem, P. Radhakrishna: Electronic Contracts, ''Tutorial'' given at ''ER 2006'', ''SCC 2007'', and ''WWW 2008''
**We delivered a detailed tutorial covering background work, the issues, open problems, and existing systems in electronic contracts. ''Tutorial slides are available on request''.
!Data Mining
*Ashish Lohia, Kirti Sinha, Soujanya Vadapalli, Kamalakar Karlapalem: An Architecture for Searching and Indexing Latex Equations in Scientific Literature. COMAD 2005: 122-130
**Mathematical equations are difficultto process and search. In this paper we processed 20,000 physics research papers formatted using Latex, extracted Latex equations and provided a search facility.
*Vipul Kedia, Vamsidhar Thummala, Kamalakar Karlapalem: Time Series Forecasting through Clustering - A Case Study. COMAD 2005: 183-191
**We have mined time series data by clustering them cumulative probability distribution functions, and exploited time series similarity for forecasting. The core technique is powerful and can be used for different applications of time series.
*Soujanya Vadapalli, Satyanarayana R. Valluri, Kamalakar Karlapalem: A Simple Yet Effective Data Clustering Algorithm. ICDM 2006: 1108-1112
**We have used the formalism of reverse nearest neighbors to come up with an effective data clustering algorithm. The main algorithm does not require any ''parameter'' to be set. This algorithm can simultaneously identify clusters and detect outliers, instead of needing seperate algorithms for clustering and outlier detection.
*Lini T. Thomas, Satyanarayana R. Valluri, Kamalakar Karlapalem: MARGIN: Maximal Frequent Subgraph Mining. ICDM 2006: 1097-1101
**Most maximal subgraph mining solutions are either top-down or bottom-up approaches. We have designed a new algorithm that is an inside-out technique where we find one maximal subgraph and use it to find all other maximal subgraphs. This is useful for efficiently finding large maximal subgraphs.
!Conceptual Modeling
*Asterio K. Tanaka, Shamkant B. Navathe, Sharma Chakravarthy, Kamalakar Karlapalem: ER-R: An Enhanced ER Model with Situation-Action Rules to Capture Application Semantics. ER 1991: 59-75
**We extended the ER model to represent and visualize ECA rules.
*Dickson K. W. Chiu, Qing Li, Kamalakar Karlapalem: A Meta Modeling Approach to Workflow Management Systems Supporting Exception Handling. Inf. Syst. 24(2): 159-184 (1999)
**We developed the notion of driving an implementation of workflow management systems to support advanced features (such as exception handling) by using meta-models. The control, monitoring, and enactment with execution level detailing becomes apparent by using the meta-modeling approach. This is a powerful technique to bring in integration from the conceptual to physical layers of a system.
*P. Radha Krishna, Kamalakar Karlapalem: Actively Evolving Conceptual Models for Mini-World and Run-Time Environment Changes. Active Conceptual Modeling of Learning 2006: 57-71
**We proposed that active conceptual modeling is required to seamlessly map the changes at higher levels all the way to changes in the current enacted environment. It is now feasible to develop a methodology and implement toolkits to support this evolution.
!Materialized View Design
*Jian Yang, Kamalakar Karlapalem, Qing Li: Algorithms for Materialized View Design in Data Warehousing Environment. VLDB 1997: 136-145
**We proposed a multiple view processing plan with a greedy and integer programming formulation for selecting views to be materialized and evaluated it.
*Ladjel Bellatreche, Kamalakar Karlapalem, Michel Schneider: On Efficient Storage Space Distribution Among Materialized Views and Indices in Data Warehousing Environments. CIKM 2000: 397-404
**Here we worked on the trade-off between the amount of space to be allotted to materialized views vis-a-vis indices. We developed an iterative problem formulation such that both these inter-related problems are addressed using the hill-climbing technique.
*Satyanarayana R. Valluri, Soujanya Vadapalli, Kamalakar Karlapalem: View Relevance Driven Materialized View Selection in Data Warehousing Environment. Australasian Database Conference 2002
**We have identified that views can be related to each other, and thus one view is relevant to other. We formulated the materialized view selection problem to consider view relvance, and showed the benefit of the selected views to be higher than the earlier methods.
!Workflow Management Systems
*Sharma Chakravarthy, Kamalakar Karlapalem, Shamkant B. Navathe, Asterio K. Tanaka: Database Supported Cooperative Problem Solving. Int. J. Cooperative Inf. Syst. 2(3): 249-288 (1993)
**We analyzed the problem of cooperative problem solving (CPS), and proposed a database supported solution for facilitating CPS using ECA rules and the notion of capabilities, and matching problem solving agents to tasks.
*Kamalakar Karlapalem, Helen P. Yeung, Patrick C. K. Hung: CapBasED-AMS - A Framework for Capability-Based and Event-Driven Activity Management System. CoopIS 1995: 205-219
**We implemented a workflow management system that is driven by ECA rules, and wherein agents are assigned to tasks based on capabilities. We separated the specification and design part of workflow, and the execution part. We illustrated the mapping of specification to implementation level relations for progressing and monitoring the execution of the workflows. We presented enough details to implement a workflow management system.
*Patrick C. K. Hung, Kamalakar Karlapalem, James W. Gray III: Least Privilege Security in CapBasED-AMS. Int. J. Cooperative Inf. Syst. 8(2-3): 139-168 (1999)
**We addressed the problem of assigning priveleges to agents to execute the tasks was addressed. The notion of least privelege security was formulated, and designed algorithms to assign agents to tasks, such that there is balance between amount of process knowledge acquired by users vis-a-vis failure resilience for task completion.
*Eleanna Kafeza, Kamalakar Karlapalem: Speeding Up Electronic Commerce Activities Using CapBasED-AMS. WECWIS 2000: 192-199
**Given a set of concurrently executing workflows, the tasks of the workflow get queued up at various agents. In this paper, we addressed the problem of speeding up workflow execution by getting tasks of the workflows to synchronously jump the queues for their respective agents. We developed algorithms to attain this, and addressed issues of fairness, possibility of speeding up, and speeding up workflows that were at some point of time delayed.
!Partitioning Object Oriented Databases
*Kamalakar Karlapalem, Shamkant B. Navathe, Magdi M. A. Morsi: Issues in Distribution Design of Object-Oriented Databases. IWDOM 1992: 148-164
**This is the first paper on reporting the issues that need to be addressed in partioning object-oriented databases. Partitioning is done to reduce the amount of irrelevant data that needs to be accessed to process user requests.
*Kamalakar Karlapalem, Qing Li: A Framework for Class Partitioning in Object-Oriented Databases. Distributed and Parallel Databases 8(3): 333-366 (2000)
**We detailed the different kinds of partitioning that can be done on object-oriented databases - across class, and composition hierarchies. We developed the object representation of paritioned classes. This paper is a good starting point to get into the area.
*Chi-Wai Fung, Kamalakar Karlapalem, Qing Li: An Evaluation of Vertical Class Partitioning for Query Processing in Object-Oriented Databases. IEEE Trans. Knowl. Data Eng. 14(5): 1095-1118 (2002)
**We developed a comprehensive analytical cost model for processing queries on vertically partitioned OODB classes. We presneted a set of analytical evaluation results to show the effect of vertical partitioning, and to study the trade-off between the projection ratio versus selectivity factor vis-a-vis sequential versus index access. Furthermore, we implemented an empirical experimental prototype supporting vertical class partitioning on a commercial OODB tool kit to validate our analytical cost model. The cost models presented can be used to design new vertical paritioing algorithms.
*Chi-Wai Fung, Kamalakar Karlapalem, Qing Li: Cost-driven vertical class partitioning for methods in object oriented databases. VLDB J. 12(3): 187-210 (2003)
**We presented two algorithms for deriving optimal and near-optimal vertical class partitioning schemes. The cost-driven algorithm provides the optimal vertical class partitioning schemes by enumerating, exhaustively, all the schemes and calculating the number of disk accesses required to execute a given set of applications. A hill-climbing heuristic algorithm (HCHA) was developed, which takes the solution provided by the affinity-based algorithm and improved it, thereby further reducing the total number of disk accesses incurred. We showed that the HCHA algorithm provides a reasonable near-optimal vertical class partitioning scheme for executing a given set of applications.
!Distributed Database Design
*Kamalakar Karlapalem, Shamkant B. Navathe, Mostafa H. Ammar: Optimal Redesign Policies to Support Dynamic Processing of Applications on a Distributed Relational Database System. Inf. Syst. 21(4): 353-367 (1996)
**We presented a dynamic redesign of distributed databases, based on probabilistic behavior of the order in which applications can be executed. We applied Markovian Decision Processes to come up with an optimal redesign policy.
*Kamalakar Karlapalem, Ng Moon Pun: Query-Driven Data Allocation Algorithms for Distributed Database Systems. DEXA 1997: 347-356
**We mapped the data allocation problem to the max-flow/min-cut problem for a single-site query processing strategy, and extended it to support the move-small query processing strategy. This is a very neat and simple solution for the data allocation problem in distributed database systems.
*Siu-Kai So, Ishfaq Ahmad, Kamalakar Karlapalem: Response Time Driven Multimedia Data Objects Allocation for Browsing Documents in Distributed Environments. IEEE Trans. Knowl. Data Eng. 11(3): 386-405 (1999)
**We addressed the problem of allocating multimedia objects to support a given QOS requirement for their presentation to end-users. Given multiple presentations of the multimedia objects with temporal constraints, we formulated the problem and came up with a hill-climbing approach to allocate the objects to various sites to meet the response time constraints.
!Potpouri
*Haresh Gurnani, Kamalakar Karlapalem: Optimal pricing strategies for Internet-based software dissemination. Journal of Operation Research Society 2001:64-70
**We used a monopoly pricing model to examine the optimal pricing strategies for 'selling' and 'pay-per-use' licensing of packaged software over the Internet. We examined the following issues in this paper: (i) what are the extra benefits to the software vendor for providing the pay-per-use option, and (ii) change in the market size. We showed that pay-per-use is a viable alternative for a large number of customers, and that judicious pricing for pay-per-use is profitable for the software vendor.
*Ritesh Tiwari, Kamalakar Karlapalem: Cost Tradeoffs for Information Security Assurance. Workshop on Economics of Information Security, 2005
**In this paper, we listed the issues and trade-offs in providing information security assurance. The focus was on investment and opportunity costs.
*Kamalakar Karlapalem: [[Optimizing Life|http://www.iiit.ac.in/~kamal/kamal.pdf]], in National Convention on Value Education and Jeevan Vidya, 2007
**I presented about some aspects of optimizing life and Jeevan Vidya.
''Professor, Head - Centre for Data Engineering, Dean (Academics)''
__''Kamalakar Karlapalem''__
''
!Spring 2008
**Database Management Systems
!Monsoon 2007
**Computer Programming
**Data Warehousing and Data Mining
**Multi Agent Systems
''