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Ullas Nambiar

Bio: Ullas Nambiar is an academic researcher from IBM. The author has contributed to research in topics: Context (language use) & Query optimization. The author has an hindex of 20, co-authored 63 publications receiving 1186 citations. Previous affiliations of Ullas Nambiar include University of California, Davis & Arizona State University.


Papers
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Patent
16 Oct 2007
TL;DR: In this article, a call in the form of an unstructured voice signal is received from a caller at a call center, the received call is transcribed into readable text data, and keywords are identified in the readable texts to determine a context for the voice signal based on identified keywords, based on the context identifying and extracting matching entities from a data store, and presenting the extracted entities and possible new queries to the call center agent based on a set of most relevant entities.
Abstract: A method and system for call processing to assist call center agents at a call center, where a call in the form of an unstructured voice signal is received from a caller at the call center, the received call is transcribed into readable text data, and keywords are identified in the readable text data to determine a context for the voice signal based on the identified keywords, based on the context identifying and extracting matching entities from a data store, and presenting the extracted entities and possible new queries to the call center agent based on the set of most relevant entities.

147 citations

Proceedings ArticleDOI
26 Oct 2008
TL;DR: This paper proposes minimum-effort driven navigational techniques for enterprise database systems based on the faceted search paradigm that dynamically suggest facets for drilling down into the database such that the cost of navigation is minimized.
Abstract: In this paper, we propose minimum-effort driven navigational techniques for enterprise database systems based on the faceted search paradigm. Our proposed techniques dynamically suggest facets for drilling down into the database such that the cost of navigation is minimized. At every step, the system asks the user a question or a set of questions on different facets and depending on the user response, dynamically fetches the next most promising set of facets, and the process repeats. Facets are selected based on their ability to rapidly drill down to the most promising tuples, as well as on the ability of the user to provide desired values for them. Our facet selection algorithms also work in conjunction with any ranked retrieval model where a ranking function imposes a bias over the user preferences for the selected tuples. Our methods are principled as well as efficient, and our experimental study validates their effectiveness on several application scenarios.

107 citations

Patent
Himanshu Gupta1, Rajeev Gupta1, Laurent Mignet1, Mukesh K. Mohania1, Ullas Nambiar1 
30 Jan 2010
TL;DR: In this paper, the authors present a data management solution that goes beyond the traditional warehousing system to support advanced analytics, including data extraction from an existing data warehouse, storing the extracted data in a reusable (intermediate) form using data parallel and compute parallel techniques over cloud, query processing over the data with/without compute parallel technique, and querying using high level querying languages.
Abstract: Embodiments of the invention provide data management solutions that go beyond the traditional warehousing system to support advanced analytics. Furthermore, embodiments of the invention relate to systems and methods for extracting data from an existing data warehouse, storing the extracted data in a reusable (intermediate) form using data parallel and compute parallel techniques over cloud, query processing over the data with/without compute parallel techniques, and providing querying using high level querying languages.

71 citations

Proceedings ArticleDOI
03 Apr 2006
TL;DR: AIMQ, a domain and user independent approach for answering imprecise queries over autonomous Web databases is presented, and methods for query relaxation that use approximate functional dependencies and an approach to automatically estimate the similarity between values of categorical attributes are presented.
Abstract: Current approaches for answering queries with imprecise constraints require user-specific distance metrics and importance measures for attributes of interest - metrics that are hard to elicit from lay users. We present AIMQ, a domain and user independent approach for answering imprecise queries over autonomous Web databases. We developed methods for query relaxation that use approximate functional dependencies. We also present an approach to automatically estimate the similarity between values of categorical attributes. Experimental results demonstrating the robustness, efficiency and effectiveness of AIMQ are presented. Results of a preliminary user study demonstrating the high precision of the AIMQ system is also provided.

67 citations

Proceedings ArticleDOI
05 Oct 2001
TL;DR: This paper proposes XOO7, an XML version of the OO7 benchmark, a pragmatic first step toward the systematic benchmarking of XML query processing platforms with an initial focus on the data (versus document) point of view.
Abstract: If XML is to play the critical role of the lingua franca for Internet data interchange that many predict, it is necessary to start designing and adopting benchmarks allowing the comparative performance analysis of the tools being developed and proposed. The effectiveness of existing XML query languages has been studied by many, with a focus on the comparison of linguistic features, implicitly reflecting the fact that most XML tools exist only on paper. In this paper, with a focus on efficiency and concreteness, we propose a pragmatic first step toward the systematic benchmarking of XML query processing platforms with an initial focus on the data (versus document) point of view. We propose XOO7, an XML version of the OO7 benchmark. We discuss the applicability of XOO7, its strengths, limitations and the extensions we are considering. We illustrate its use by presenting and discussing the performance comparison against XOO7 of three different query processing platforms for XML.

64 citations


Cited by
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Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

01 Jan 2002

9,314 citations

Patent
11 Jan 2011
TL;DR: In this article, an intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions.
Abstract: An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

1,462 citations

Book ChapterDOI
20 Aug 2002
TL;DR: This work provides a framework to assess the abilities of an XML database to cope with a broad range of different query types typically encountered in real-world scenarios and offers a set of queries where each query is intended to challenge a particular aspect of the query processor.
Abstract: While standardization efforts for XML query languages have been progressing, researchers and users increasingly focus on the database technology that has to deliver on the new challenges that the abundance of XML documents poses to data management: validation, performance evaluation and optimization of XML query processors are the upcoming issues. Following a long tradition in database research, we provide a framework to assess the abilities of an XML database to cope with a broad range of different query types typically encountered in real-world scenarios. The benchmark can help both implementors and users to compare XML databases in a standardized application scenario. To this end, we offer a set of queries where each query is intended to challenge a particular aspect of the query processor. The overall workload we propose consists of a scalable document database and a concise, yet comprehensive set of queries which covers the major aspects of XML query processing ranging from textual features to data analysis queries and ad hoc queries. We complement our research with results we obtained from running the benchmark on several XML database platforms. These results are intended to give a first baseline and illustrate the state of the art.

822 citations

Patent
28 Sep 2012
TL;DR: In this article, a virtual assistant uses context information to supplement natural language or gestural input from a user, which helps to clarify the user's intent and reduce the number of candidate interpretations of user's input, and reduces the need for the user to provide excessive clarification input.
Abstract: A virtual assistant uses context information to supplement natural language or gestural input from a user. Context helps to clarify the user's intent and to reduce the number of candidate interpretations of the user's input, and reduces the need for the user to provide excessive clarification input. Context can include any available information that is usable by the assistant to supplement explicit user input to constrain an information-processing problem and/or to personalize results. Context can be used to constrain solutions during various phases of processing, including, for example, speech recognition, natural language processing, task flow processing, and dialog generation.

593 citations