arXiv subjects

Naren Ramakrishnan

Publications and source records attributed to Naren Ramakrishnan.

123 records · Page 7Linked to original sources

arXiv2003

Novel Runtime Systems Support for Adaptive Compositional Modeling on the Grid

Grid infrastructures and computing environments have progressed significantly in the past few years. The vision of truly seamless Grid usage relies on runtime systems support that is cognizant of the operational issues underlying grid computations and, at the same time, is flexible enough to accommodate diverse application scenarios. This paper addresses the twin aspects of Grid infrastructure and application support through a novel combination of two computational technologies: Weaves - a source-language independent parallel runtime compositional framework that operates through reverse-analysis of compiled object files, and runtime recommender systems that aid in dynamic knowledge-based application composition. Domain-specific adaptivity is exploited through a novel compositional system that supports runtime recommendation of code modules and a sophisticated checkpointing and runtime migration solution that can be transparently deployed over Grid infrastructures. A core set of "adaptivity schemas" are provided as templates for adaptive composition of large-scale scientific computations. Implementation issues, motivating application contexts, and preliminary results are described.

cs.CE↗

arXiv2002

Using Hierarchical Data Mining to Characterize Performance of Wireless System Configurations

This paper presents a statistical framework for assessing wireless systems performance using hierarchical data mining techniques. We consider WCDMA (wideband code division multiple access) systems with two-branch STTD (space time transmit diversity) and 1/2 rate convolutional coding (forward error correction codes). Monte Carlo simulation estimates the bit error probability (BEP) of the system across a wide range of signal-to-noise ratios (SNRs). A performance database of simulation runs is collected over a targeted space of system configurations. This database is then mined to obtain regions of the configuration space that exhibit acceptable average performance. The shape of the mined regions illustrates the joint influence of configuration parameters on system performance. The role of data mining in this application is to provide explainable and statistically valid design conclusions. The research issue is to define statistically meaningful aggregation of data in a manner that permits efficient and effective data mining algorithms. We achieve a good compromise between these goals and help establish the applicability of data mining for characterizing wireless systems performance.

cs.CE↗

arXiv2002

Reinforcing Reachable Routes

This paper studies the evaluation of routing algorithms from the perspective of reachability routing, where the goal is to determine all paths between a sender and a receiver. Reachability routing is becoming relevant with the changing dynamics of the Internet and the emergence of low-bandwidth wireless/ad-hoc networks. We make the case for reinforcement learning as the framework of choice to realize reachability routing, within the confines of the current Internet infrastructure. The setting of the reinforcement learning problem offers several advantages, including loop resolution, multi-path forwarding capability, cost-sensitive routing, and minimizing state overhead, while maintaining the incremental spirit of current backbone routing algorithms. We identify research issues in reinforcement learning applied to the reachability routing problem to achieve a fluid and robust backbone routing framework. The paper is targeted toward practitioners seeking to implement a reachability routing algorithm.

cs.NI↗

arXiv2002

The Traits of the Personable

Information personalization is fertile ground for application of AI techniques. In this article I relate personalization to the ability to capture partial information in an information-seeking interaction. The specific focus is on personalizing interactions at web sites. Using ideas from partial evaluation and explanation-based generalization, I present a modeling methodology for reasoning about personalization. This approach helps identify seven tiers of `personable traits' in web sites.

cs.AI↗

arXiv2002

Weaves: A Novel Direct Code Execution Interface for Parallel High Performance Scientific Codes

Scientific codes are increasingly being used in compositional settings, especially problem solving environments (PSEs). Typical compositional modeling frameworks require significant buy-in, in the form of commitment to a particular style of programming (e.g., distributed object components). While this solution is feasible for newer generations of component-based scientific codes, large legacy code bases present a veritable software engineering nightmare. We introduce Weaves a novel framework that enables modeling, composition, direct code execution, performance characterization, adaptation, and control of unmodified high performance scientific codes. Weaves is an efficient generalized framework for parallel compositional modeling that is a proper superset of the threads and processes models of programming. In this paper, our focus is on the transparent code execution interface enabled by Weaves. We identify design constraints, their impact on implementation alternatives, configuration scenarios, and present results from a prototype implementation on Intel x86 architectures.

cs.DC↗

arXiv2002

Qualitative Analysis of Correspondence for Experimental Algorithmics

Correspondence identifies relationships among objects via similarities among their components; it is ubiquitous in the analysis of spatial datasets, including images, weather maps, and computational simulations. This paper develops a novel multi-level mechanism for qualitative analysis of correspondence. Operators leverage domain knowledge to establish correspondence, evaluate implications for model selection, and leverage identified weaknesses to focus additional data collection. The utility of the mechanism is demonstrated in two applications from experimental algorithmics -- matrix spectral portrait analysis and graphical assessment of Jordan forms of matrices. Results show that the mechanism efficiently samples computational experiments and successfully uncovers high-level problem properties. It overcomes noise and data sparsity by leveraging domain knowledge to detect mutually reinforcing interpretations of spatial data.

cs.AI↗

arXiv2002

Sampling Strategies for Mining in Data-Scarce Domains

Data mining has traditionally focused on the task of drawing inferences from large datasets. However, many scientific and engineering domains, such as fluid dynamics and aircraft design, are characterized by scarce data, due to the expense and complexity of associated experiments and simulations. In such data-scarce domains, it is advantageous to focus the data collection effort on only those regions deemed most important to support a particular data mining objective. This paper describes a mechanism that interleaves bottom-up data mining, to uncover multi-level structures in spatial data, with top-down sampling, to clarify difficult decisions in the mining process. The mechanism exploits relevant physical properties, such as continuity, correspondence, and locality, in a unified framework. This leads to effective mining and sampling decisions that are explainable in terms of domain knowledge and data characteristics. This approach is demonstrated in two diverse applications -- mining pockets in spatial data, and qualitative determination of Jordan forms of matrices.

cs.CE↗

arXiv2002

BSML: A Binding Schema Markup Language for Data Interchange in Problem Solving Environments (PSEs)

We describe a binding schema markup language (BSML) for describing data interchange between scientific codes. Such a facility is an important constituent of scientific problem solving environments (PSEs). BSML is designed to integrate with a PSE or application composition system that views model specification and execution as a problem of managing semistructured data. The data interchange problem is addressed by three techniques for processing semistructured data: validation, binding, and conversion. We present BSML and describe its application to a PSE for wireless communications system design.

cs.CE↗

arXiv2001

Explaining Scenarios for Information Personalization

Personalization customizes information access. The PIPE ("Personalization is Partial Evaluation") modeling methodology represents interaction with an information space as a program. The program is then specialized to a user's known interests or information seeking activity by the technique of partial evaluation. In this paper, we elaborate PIPE by considering requirements analysis in the personalization lifecycle. We investigate the use of scenarios as a means of identifying and analyzing personalization requirements. As our first result, we show how designing a PIPE representation can be cast as a search within a space of PIPE models, organized along a partial order. This allows us to view the design of a personalization system, itself, as specialized interpretation of an information space. We then exploit the underlying equivalence of explanation-based generalization (EBG) and partial evaluation to realize high-level goals and needs identified in scenarios; in particular, we specialize (personalize) an information space based on the explanation of a user scenario in that information space, just as EBG specializes a theory based on the explanation of an example in that theory. In this approach, personalization becomes the transformation of information spaces to support the explanation of usage scenarios. An example application is described.

cs.HC↗

arXiv2001

The Expresso Microarray Experiment Management System: The Functional Genomics of Stress Responses in Loblolly Pine

Conception, design, and implementation of cDNA microarray experiments present a variety of bioinformatics challenges for biologists and computational scientists. The multiple stages of data acquisition and analysis have motivated the design of Expresso, a system for microarray experiment management. Salient aspects of Expresso include support for clone replication and randomized placement; automatic gridding, extraction of expression data from each spot, and quality monitoring; flexible methods of combining data from individual spots into information about clones and functional categories; and the use of inductive logic programming for higher-level data analysis and mining. The development of Expresso is occurring in parallel with several generations of microarray experiments aimed at elucidating genomic responses to drought stress in loblolly pine seedlings. The current experimental design incorporates 384 pine cDNAs replicated and randomly placed in two specific microarray layouts. We describe the design of Expresso as well as results of analysis with Expresso that suggest the importance of molecular chaperones and membrane transport proteins in mechanisms conferring successful adaptation to long-term drought stress.

cs.OH↗

arXiv2001

Mixed-Initiative Interaction = Mixed Computation

We show that partial evaluation can be usefully viewed as a programming model for realizing mixed-initiative functionality in interactive applications. Mixed-initiative interaction between two participants is one where the parties can take turns at any time to change and steer the flow of interaction. We concentrate on the facet of mixed-initiative referred to as `unsolicited reporting' and demonstrate how out-of-turn interactions by users can be modeled by `jumping ahead' to nested dialogs (via partial evaluation). Our approach permits the view of dialog management systems in terms of their native support for staging and simplifying interactions; we characterize three different voice-based interaction technologies using this viewpoint. In particular, we show that the built-in form interpretation algorithm (FIA) in the VoiceXML dialog management architecture is actually a (well disguised) combination of an interpreter and a partial evaluator.

cs.PL↗

arXiv2001

The Partial Evaluation Approach to Information Personalization

Information personalization refers to the automatic adjustment of information content, structure, and presentation tailored to an individual user. By reducing information overload and customizing information access, personalization systems have emerged as an important segment of the Internet economy. This paper presents a systematic modeling methodology - PIPE (`Personalization is Partial Evaluation') - for personalization. Personalization systems are designed and implemented in PIPE by modeling an information-seeking interaction in a programmatic representation. The representation supports the description of information-seeking activities as partial information and their subsequent realization by partial evaluation, a technique for specializing programs. We describe the modeling methodology at a conceptual level and outline representational choices. We present two application case studies that use PIPE for personalizing web sites and describe how PIPE suggests a novel evaluation criterion for information system designs. Finally, we mention several fundamental implications of adopting the PIPE model for personalization and when it is (and is not) applicable.

cs.IR↗

arXiv2001

When being Weak is Brave: Privacy in Recommender Systems

We explore the conflict between personalization and privacy that arises from the existence of weak ties. A weak tie is an unexpected connection that provides serendipitous recommendations. However, information about weak ties could be used in conjunction with other sources of data to uncover identities and reveal other personal information. In this article, we use a graph-theoretic model to study the benefit and risk from weak ties.

cs.CR↗

arXiv2001

Evaluating Recommendation Algorithms by Graph Analysis

We present a novel framework for evaluating recommendation algorithms in terms of the `jumps' that they make to connect people to artifacts. This approach emphasizes reachability via an algorithm within the implicit graph structure underlying a recommender dataset, and serves as a complement to evaluation in terms of predictive accuracy. The framework allows us to consider questions relating algorithmic parameters to properties of the datasets. For instance, given a particular algorithm `jump,' what is the average path length from a person to an artifact? Or, what choices of minimum ratings and jumps maintain a connected graph? We illustrate the approach with a common jump called the `hammock' using movie recommender datasets.

cs.IR↗

arXiv2000

PIPE: Personalizing Recommendations via Partial Evaluation

It is shown that personalization of web content can be advantageously viewed as a form of partial evaluation --- a technique well known in the programming languages community. The basic idea is to model a recommendation space as a program, then partially evaluate this program with respect to user preferences (and features) to obtain specialized content. This technique supports both content-based and collaborative approaches, and is applicable to a range of applications that require automatic information integration from multiple web sources. The effectiveness of this methodology is illustrated by two example applications --- (i) personalizing content for visitors to the Blacksburg Electronic Village (http://www.bev.net), and (ii) locating and selecting scientific software on the Internet. The scalability of this technique is demonstrated by its ability to interface with online web ontologies that index thousands of web pages.

cs.IR↗