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Abstract:
The focus of my research is on enabling novel kinds of interaction between the user and the information in a variety of digital environments, ranging from social content sites, to digital libraries, to the Web. In the first part of this talk, I will present an approach for tracking and querying fine-grained provenance in data-intensive workflows. A workflow is an encoding of a sequence of steps that progressively transform data products. Workflows help make experiments reproducible, and may be used to answer questions about data provenance â the dependencies between input, intermediate, and output data. I will describe a declarative framework that captures fine-grained dependencies, enabling novel kinds of analytic queries, and will demonstrate that careful design and leveraging distributed processing make tracking and querying fine-grained provenance feasible. In the second part of this talk, I will discuss information discovery on the Social Web, where users provide information about themselves in stored profiles, register their relationships with other users, and express their preferences with respect to information and products. I will argue that information discovery should account for a user's social context, and will present network-aware search â a novel search paradigm in which result relevance is computed with respect to a user's social network. I will describe efficient algorithms appropriate for this setting, and will show how social similarities between users may be leveraged to make processing more efficient. |
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Abstract:
The focus of my research is on enabling novel kinds of interaction between the user and the information in a variety of digital environments, ranging from social content sites, to digital libraries, to the Web. In the first part of this talk, I will present an approach for tracking and querying fine-grained provenance in data-intensive workflows. A workflow is an encoding of a sequence of steps that progressively transform data products. Workflows help make experiments reproducible, and may be used to answer questions about data provenance – the dependencies between input, intermediate, and output data. I will describe a declarative framework that captures fine-grained dependencies, enabling novel kinds of analytic queries, and will demonstrate that careful design and leveraging distributed processing make tracking and querying fine-grained provenance feasible. In the second part of this talk, I will discuss information discovery on the Social Web, where users provide information about themselves in stored profiles, register their relationships with other users, and express their preferences with respect to information and products. I will argue that information discovery should account for a user's social context, and will present network-aware search – a novel search paradigm in which result relevance is computed with respect to a user's social network. I will describe efficient algorithms appropriate for this setting, and will show how social similarities between users may be leveraged to make processing more efficient. |