�ݺ�ߣshows by User: debdootmukherjee

�ݺ�ߣshows by User: debdootmukherjee / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: debdootmukherjee / Sun, 02 Aug 2015 16:07:25 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: debdootmukherjee meetup-talk /slideshow/meetuptalk/51191577 43d70ca0-f07d-41c7-972d-27b6507fb20f-150802160725-lva1-app6892
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]]> Sun, 02 Aug 2015 16:07:25 GMT /slideshow/meetuptalk/51191577 debdootmukherjee@slideshare.net(debdootmukherjee) meetup-talk debdootmukherjee <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/43d70ca0-f07d-41c7-972d-27b6507fb20f-150802160725-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" />

meetup-talk from Debdoot Mukherjee

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0 Serving Information Needs of Knowledge Workers /slideshow/serving-information-needs-of-knowledge-workers/44355470 92a2f014-f28d-478d-af3d-6e370ce6603c-150206101736-conversion-gate01
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]]> Fri, 06 Feb 2015 10:17:36 GMT /slideshow/serving-information-needs-of-knowledge-workers/44355470 debdootmukherjee@slideshare.net(debdootmukherjee) Serving Information Needs of Knowledge Workers debdootmukherjee <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/92a2f014-f28d-478d-af3d-6e370ce6603c-150206101736-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" />

Serving Information Needs of Knowledge Workers from Debdoot Mukherjee

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0 Determining QoS of WS-BPEL Compositions /debdootmukherjee/conf-talk conftalk-130514074836-phpapp01
With a large number of web services offering the same functionality, the Quality of Service (QoS) rendered by a web service becomes a key differentiator. WS-BPEL has emerged as the de facto industry standard for composing web services. Thus, determining the QoS of a composite web service expressed in BPEL can be extremely beneficial. While there has been much work on QoS computation of structured workflows, there exists no tool to ascertain QoS for BPEL processes, which are semantically richer than conventional workflows. We propose a model for estimating three key QoS parameters - Response Time, Cost and Reliability - of an executable BPEL process from the QoS information of its partner services and certain control flow parameters. We have built a tool to compute QoS of a WS-BPEL process that accounts for most workflow patterns that may be expressed by standard WS-BPEL. Another feature of our QoS approach and the tool is that it allows a designer to explore the impact on QoS of using different software fault tolerance techniques like Recovery blocks, N-version programming etc., thereby provisioning QoS computation of mission critical applications that may employ these techniques to achieve high reliability and/or performance.]]>
With a large number of web services offering the same functionality, the Quality of Service (QoS) rendered by a web service becomes a key differentiator. WS-BPEL has emerged as the de facto industry standard for composing web services. Thus, determining the QoS of a composite web service expressed in BPEL can be extremely beneficial. While there has been much work on QoS computation of structured workflows, there exists no tool to ascertain QoS for BPEL processes, which are semantically richer than conventional workflows. We propose a model for estimating three key QoS parameters - Response Time, Cost and Reliability - of an executable BPEL process from the QoS information of its partner services and certain control flow parameters. We have built a tool to compute QoS of a WS-BPEL process that accounts for most workflow patterns that may be expressed by standard WS-BPEL. Another feature of our QoS approach and the tool is that it allows a designer to explore the impact on QoS of using different software fault tolerance techniques like Recovery blocks, N-version programming etc., thereby provisioning QoS computation of mission critical applications that may employ these techniques to achieve high reliability and/or performance.]]> Tue, 14 May 2013 07:48:36 GMT /debdootmukherjee/conf-talk debdootmukherjee@slideshare.net(debdootmukherjee) Determining QoS of WS-BPEL Compositions debdootmukherjee With a large number of web services offering the same functionality, the Quality of Service (QoS) rendered by a web service becomes a key differentiator. WS-BPEL has emerged as the de facto industry standard for composing web services. Thus, determining the QoS of a composite web service expressed in BPEL can be extremely beneficial. While there has been much work on QoS computation of structured workflows, there exists no tool to ascertain QoS for BPEL processes, which are semantically richer than conventional workflows. We propose a model for estimating three key QoS parameters - Response Time, Cost and Reliability - of an executable BPEL process from the QoS information of its partner services and certain control flow parameters. We have built a tool to compute QoS of a WS-BPEL process that accounts for most workflow patterns that may be expressed by standard WS-BPEL. Another feature of our QoS approach and the tool is that it allows a designer to explore the impact on QoS of using different software fault tolerance techniques like Recovery blocks, N-version programming etc., thereby provisioning QoS computation of mission critical applications that may employ these techniques to achieve high reliability and/or performance. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/conftalk-130514074836-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> With a large number of web services offering the same functionality, the Quality of Service (QoS) rendered by a web service becomes a key differentiator. WS-BPEL has emerged as the de facto industry standard for composing web services. Thus, determining the QoS of a composite web service expressed in BPEL can be extremely beneficial. While there has been much work on QoS computation of structured workflows, there exists no tool to ascertain QoS for BPEL processes, which are semantically richer than conventional workflows. We propose a model for estimating three key QoS parameters - Response Time, Cost and Reliability - of an executable BPEL process from the QoS information of its partner services and certain control flow parameters. We have built a tool to compute QoS of a WS-BPEL process that accounts for most workflow patterns that may be expressed by standard WS-BPEL. Another feature of our QoS approach and the tool is that it allows a designer to explore the impact on QoS of using different software fault tolerance techniques like Recovery blocks, N-version programming etc., thereby provisioning QoS computation of mission critical applications that may employ these techniques to achieve high reliability and/or performance.

Determining QoS of WS-BPEL Compositions from Debdoot Mukherjee

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0 Scc talk /slideshow/scc-talk/21162191 scctalk-130514074220-phpapp02
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]]> Tue, 14 May 2013 07:42:20 GMT /slideshow/scc-talk/21162191 debdootmukherjee@slideshare.net(debdootmukherjee) Scc talk debdootmukherjee <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/scctalk-130514074220-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" />

Scc talk from Debdoot Mukherjee

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0 Is Text Search an Effective Approach for Fault Localization: A Practitioners Perspective /slideshow/is-text-search-an-effective-approach-for-fault-localization-a-practitioners-perspective-21162101/21162101 splash2012-text-search-130514073913-phpapp01
There has been widespread interest in both academia and industry around techniques to help in fault localization. Much of this work leverages static or dynamic code analysis and hence is constrained by the programming language used or presence of test cases. In order to provide more generically applicable techniques, recent work has focused on devising text search based approaches that recommend source files which a developer can modify to fix a bug. Text search may be used for fault localization in either of the following ways. We can search a repository of past bugs with the bug description to find similar bugs and recommend the source files that were modified to fix those bugs. Alternately, we can directly search the code repository to find source files that share words with the bug report text. Few interesting questions come to mind when we consider applying these text-based search techniques in real projects. For example, would searching on past fixed bugs yield better results than searching on code? What is the accuracy one can expect? Would giving preference to code words in the bug report better the search results? In this paper, we apply variants of text-search on four open source projects and compare the impact of different design considerations on search efficacy. ]]>
There has been widespread interest in both academia and industry around techniques to help in fault localization. Much of this work leverages static or dynamic code analysis and hence is constrained by the programming language used or presence of test cases. In order to provide more generically applicable techniques, recent work has focused on devising text search based approaches that recommend source files which a developer can modify to fix a bug. Text search may be used for fault localization in either of the following ways. We can search a repository of past bugs with the bug description to find similar bugs and recommend the source files that were modified to fix those bugs. Alternately, we can directly search the code repository to find source files that share words with the bug report text. Few interesting questions come to mind when we consider applying these text-based search techniques in real projects. For example, would searching on past fixed bugs yield better results than searching on code? What is the accuracy one can expect? Would giving preference to code words in the bug report better the search results? In this paper, we apply variants of text-search on four open source projects and compare the impact of different design considerations on search efficacy. ]]> Tue, 14 May 2013 07:39:13 GMT /slideshow/is-text-search-an-effective-approach-for-fault-localization-a-practitioners-perspective-21162101/21162101 debdootmukherjee@slideshare.net(debdootmukherjee) Is Text Search an Effective Approach for Fault Localization: A Practitioners Perspective debdootmukherjee There has been widespread interest in both academia and industry around techniques to help in fault localization. Much of this work leverages static or dynamic code analysis and hence is constrained by the programming language used or presence of test cases. In order to provide more generically applicable techniques, recent work has focused on devising text search based approaches that recommend source files which a developer can modify to fix a bug. Text search may be used for fault localization in either of the following ways. We can search a repository of past bugs with the bug description to find similar bugs and recommend the source files that were modified to fix those bugs. Alternately, we can directly search the code repository to find source files that share words with the bug report text. Few interesting questions come to mind when we consider applying these text-based search techniques in real projects. For example, would searching on past fixed bugs yield better results than searching on code? What is the accuracy one can expect? Would giving preference to code words in the bug report better the search results? In this paper, we apply variants of text-search on four open source projects and compare the impact of different design considerations on search efficacy. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/splash2012-text-search-130514073913-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> There has been widespread interest in both academia and industry around techniques to help in fault localization. Much of this work leverages static or dynamic code analysis and hence is constrained by the programming language used or presence of test cases. In order to provide more generically applicable techniques, recent work has focused on devising text search based approaches that recommend source files which a developer can modify to fix a bug. Text search may be used for fault localization in either of the following ways. We can search a repository of past bugs with the bug description to find similar bugs and recommend the source files that were modified to fix those bugs. Alternately, we can directly search the code repository to find source files that share words with the bug report text. Few interesting questions come to mind when we consider applying these text-based search techniques in real projects. For example, would searching on past fixed bugs yield better results than searching on code? What is the accuracy one can expect? Would giving preference to code words in the bug report better the search results? In this paper, we apply variants of text-search on four open source projects and compare the impact of different design considerations on search efficacy.

Is Text Search an Effective Approach for Fault Localization: A Practitioners Perspective from Debdoot Mukherjee

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0 Is Text Search an Effective Approach for Fault Localization: A Practitioners Perspective /slideshow/is-text-search-an-effective-approach-for-fault-localization-a-practitioners-perspective/21162068 splash2012-text-search-130514073817-phpapp01
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]]> Tue, 14 May 2013 07:38:16 GMT /slideshow/is-text-search-an-effective-approach-for-fault-localization-a-practitioners-perspective/21162068 debdootmukherjee@slideshare.net(debdootmukherjee) Is Text Search an Effective Approach for Fault Localization: A Practitioners Perspective debdootmukherjee <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/splash2012-text-search-130514073817-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" />

Is Text Search an Effective Approach for Fault Localization: A Practitioners Perspective from Debdoot Mukherjee

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0 Which Work-Item Updates Need Your Response? /slideshow/twiny-msr/21161845 twiny-msr-130514073125-phpapp01
Work-item notifications alert the team collaborating on a work-item about any update to the work-item (e.g., addition of comments, change in status). However, as software professionals get involved with multiple tasks in project(s), they are inundated by too many notifications from the work-item tool. Users are upset that they often miss the notifications that solicit their response in the crowd of mostly useless ones. We investigate the severity of this problem by studying the work-item repositories of two large collaborative projects and conducting a user study with one of the project teams. We find that, on an average, only 1 out of every 5 notifications that are received by the users require a response from them. We propose TWINY -- a machine learning based approach to predict whether a notification will prompt any action from its recipient. Such a prediction can help to suitably mark up notifications and to decide whether a notification needs to be sent out immediately or be bundled in a message digest. We conduct empirical studies to evaluate the efficacy of different classification techniques in this setting. We find that incremental learning algorithms are ideally suited, and ensemble methods appear to give the best results in terms of prediction accuracy.]]>
Work-item notifications alert the team collaborating on a work-item about any update to the work-item (e.g., addition of comments, change in status). However, as software professionals get involved with multiple tasks in project(s), they are inundated by too many notifications from the work-item tool. Users are upset that they often miss the notifications that solicit their response in the crowd of mostly useless ones. We investigate the severity of this problem by studying the work-item repositories of two large collaborative projects and conducting a user study with one of the project teams. We find that, on an average, only 1 out of every 5 notifications that are received by the users require a response from them. We propose TWINY -- a machine learning based approach to predict whether a notification will prompt any action from its recipient. Such a prediction can help to suitably mark up notifications and to decide whether a notification needs to be sent out immediately or be bundled in a message digest. We conduct empirical studies to evaluate the efficacy of different classification techniques in this setting. We find that incremental learning algorithms are ideally suited, and ensemble methods appear to give the best results in terms of prediction accuracy.]]> Tue, 14 May 2013 07:31:25 GMT /slideshow/twiny-msr/21161845 debdootmukherjee@slideshare.net(debdootmukherjee) Which Work-Item Updates Need Your Response? debdootmukherjee Work-item notifications alert the team collaborating on a work-item about any update to the work-item (e.g., addition of comments, change in status). However, as software professionals get involved with multiple tasks in project(s), they are inundated by too many notifications from the work-item tool. Users are upset that they often miss the notifications that solicit their response in the crowd of mostly useless ones. We investigate the severity of this problem by studying the work-item repositories of two large collaborative projects and conducting a user study with one of the project teams. We find that, on an average, only 1 out of every 5 notifications that are received by the users require a response from them. We propose TWINY -- a machine learning based approach to predict whether a notification will prompt any action from its recipient. Such a prediction can help to suitably mark up notifications and to decide whether a notification needs to be sent out immediately or be bundled in a message digest. We conduct empirical studies to evaluate the efficacy of different classification techniques in this setting. We find that incremental learning algorithms are ideally suited, and ensemble methods appear to give the best results in terms of prediction accuracy. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/twiny-msr-130514073125-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Work-item notifications alert the team collaborating on a work-item about any update to the work-item (e.g., addition of comments, change in status). However, as software professionals get involved with multiple tasks in project(s), they are inundated by too many notifications from the work-item tool. Users are upset that they often miss the notifications that solicit their response in the crowd of mostly useless ones. We investigate the severity of this problem by studying the work-item repositories of two large collaborative projects and conducting a user study with one of the project teams. We find that, on an average, only 1 out of every 5 notifications that are received by the users require a response from them. We propose TWINY -- a machine learning based approach to predict whether a notification will prompt any action from its recipient. Such a prediction can help to suitably mark up notifications and to decide whether a notification needs to be sent out immediately or be bundled in a message digest. We conduct empirical studies to evaluate the efficacy of different classification techniques in this setting. We find that incremental learning algorithms are ideally suited, and ensemble methods appear to give the best results in terms of prediction accuracy.

Which Work-Item Updates Need Your Response? from Debdoot Mukherjee

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0 From Informal Process Diagrams To Formal Process Models /slideshow/from-informal-process-diagrams-to-formal-process-models/5351654 bpm2010-101004053742-phpapp01
Process modeling is an important activity in business transformation projects. Free-form diagramming tools, such as PowerPoint and Visio, are the preferred tools for creating process models. However, the designs created using such tools are informal sketches, which are not amenable to automated analysis. Formal models, although desirable, are rarely created (during early design) because of the usability problems associated with formal-modeling tools. In this paper, we present an approach for automatically inferring formal process models from informal business process diagrams, so that the strengths of both types of tools can be leveraged. We discuss different sources of structural and semantic ambiguities, commonly present in informal diagrams, which pose challenges for automated inference. Our approach consists of two phases. First, it performs structural inference to identify the set of nodes and edges that constitute a process model. Then, it performs semantic interpretation, using a classifier that mimics human reasoning to associate modeling semantics with the nodes and edges. We discuss both supervised and unsupervised techniques for training such a classifier. Finally, we report results of empirical studies, conducted using flow diagrams from real projects, which illustrate the effectiveness of our approach. ]]>
Process modeling is an important activity in business transformation projects. Free-form diagramming tools, such as PowerPoint and Visio, are the preferred tools for creating process models. However, the designs created using such tools are informal sketches, which are not amenable to automated analysis. Formal models, although desirable, are rarely created (during early design) because of the usability problems associated with formal-modeling tools. In this paper, we present an approach for automatically inferring formal process models from informal business process diagrams, so that the strengths of both types of tools can be leveraged. We discuss different sources of structural and semantic ambiguities, commonly present in informal diagrams, which pose challenges for automated inference. Our approach consists of two phases. First, it performs structural inference to identify the set of nodes and edges that constitute a process model. Then, it performs semantic interpretation, using a classifier that mimics human reasoning to associate modeling semantics with the nodes and edges. We discuss both supervised and unsupervised techniques for training such a classifier. Finally, we report results of empirical studies, conducted using flow diagrams from real projects, which illustrate the effectiveness of our approach. ]]> Mon, 04 Oct 2010 05:37:30 GMT /slideshow/from-informal-process-diagrams-to-formal-process-models/5351654 debdootmukherjee@slideshare.net(debdootmukherjee) From Informal Process Diagrams To Formal Process Models debdootmukherjee Process modeling is an important activity in business transformation projects. Free-form diagramming tools, such as PowerPoint and Visio, are the preferred tools for creating process models. However, the designs created using such tools are informal sketches, which are not amenable to automated analysis. Formal models, although desirable, are rarely created (during early design) because of the usability problems associated with formal-modeling tools. In this paper, we present an approach for automatically inferring formal process models from informal business process diagrams, so that the strengths of both types of tools can be leveraged. We discuss different sources of structural and semantic ambiguities, commonly present in informal diagrams, which pose challenges for automated inference. Our approach consists of two phases. First, it performs structural inference to identify the set of nodes and edges that constitute a process model. Then, it performs semantic interpretation, using a classifier that mimics human reasoning to associate modeling semantics with the nodes and edges. We discuss both supervised and unsupervised techniques for training such a classifier. Finally, we report results of empirical studies, conducted using flow diagrams from real projects, which illustrate the effectiveness of our approach. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/bpm2010-101004053742-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Process modeling is an important activity in business transformation projects. Free-form diagramming tools, such as PowerPoint and Visio, are the preferred tools for creating process models. However, the designs created using such tools are informal sketches, which are not amenable to automated analysis. Formal models, although desirable, are rarely created (during early design) because of the usability problems associated with formal-modeling tools. In this paper, we present an approach for automatically inferring formal process models from informal business process diagrams, so that the strengths of both types of tools can be leveraged. We discuss different sources of structural and semantic ambiguities, commonly present in informal diagrams, which pose challenges for automated inference. Our approach consists of two phases. First, it performs structural inference to identify the set of nodes and edges that constitute a process model. Then, it performs semantic interpretation, using a classifier that mimics human reasoning to associate modeling semantics with the nodes and edges. We discuss both supervised and unsupervised techniques for training such a classifier. Finally, we report results of empirical studies, conducted using flow diagrams from real projects, which illustrate the effectiveness of our approach.

From Informal Process Diagrams To Formal Process Models from Debdoot Mukherjee

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0 https://cdn.slidesharecdn.com/profile-photo-debdootmukherjee-48x48.jpg?cb=1528291354 Currently, I am leading data science programs at Myntra focused on personalization and customer analytics. Prior to this, I worked for IBM Research where I investigated problems related to enterprise search and information extraction. My research on improving knowledge worker productivity influenced several IBM software and service offerings and led to multiple IBM Research accomplishments. Till date, I have authored 18 international research publications and filed 22 patents. I have graduated from Indian Institute of Technology (IIT), Delhi where I finished at the top of the class. Specialties: Information Retrieval, User Modeling, Personalization, Information Extraction, Machine Learni... http://researcher.watson.ibm.com/researcher/view.php?person=in-debdomuk https://cdn.slidesharecdn.com/ss_thumbnails/43d70ca0-f07d-41c7-972d-27b6507fb20f-150802160725-lva1-app6892-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/meetuptalk/51191577 meetup-talk https://cdn.slidesharecdn.com/ss_thumbnails/92a2f014-f28d-478d-af3d-6e370ce6603c-150206101736-conversion-gate01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/serving-information-needs-of-knowledge-workers/44355470 Serving Information Ne... https://cdn.slidesharecdn.com/ss_thumbnails/conftalk-130514074836-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds debdootmukherjee/conf-talk Determining QoS of WS-...