The repurposing of biomedical data is inhibited by its fragmented and multi-formatted nature that requires redundant investment of time and resources by data scientists. This is particularly true for Type 1 Diabetes (T1D), one of the most intensely studied common childhood diseases. Intense investigation of the contribution of pancreatic β-islet and T-lymphocytes in T1D has been made. However, genetic contributions from B-lymphocytes, which are known to play a role in a subset of T1D patients, remain relatively understudied. We have addressed this issue through the creation of Biomedical Data Commons (BMDC), a knowledge graph that integrates data from multiple sources into a single queryable format. This increases the speed of analysis by multiple orders of magnitude. We develop a pipeline using B-lymphocyte multi-dimensional epigenome and connectome data and deploy BMDC to assess genetic variants in the context of Type 1 Diabetes (T1D). Pipeline-identified variants are primarily common, non-coding, poorly conserved, and are of unknown clinical significance. While variants and their chromatin connectivity are cell-type specific, they are associated with well-studied disease genes in T-lymphocytes. Candidates include established variants in the HLA-DQB1 and HLA-DRB1 and IL2RA loci that have previously been demonstrated to protect against T1D in humans and mice providing validation for this method. Others are included in the well-established T1D GRS2 genetic risk scoring method. More intriguingly, other prioritized variants are completely novel and form the basis for future mechanistic and clinical validation studies The BMDC community-based platform can be expanded and repurposed to increase the accessibility, reproducibility, and productivity of biomedical information for diverse applications including the prioritization of cell type-specific disease alleles from complex phenotypes.

Publicly available data from open sources are a vital resource for students and researchers in a variety of disciplines. Unfortunately, processing these datasets to make them useful --- scraping, cleaning, normalizing, joining --- is tedious, error prone and has to repeated by every group.DataCommons attempts to alleviate some of this pain by synthesizing a single Knowledge Graph from many different data sources. It links references to the same entities (such as cities, counties, organizations, etc.) across different datasets to nodes on the graph, so that users can access data about a particular entity aggregated from different sources. Like the Web, the DataCommons graph is open - any user can contribute data or build applications powered by the graph. In the Google DataCommons, we are jump-starting the graph with data from publicly available sources such as CDC, Census, BLS, FBI, etc. and are looking to engage with the academic community to take it further.

We present a user modeling system that serves as the foundation of a personal assistant. The system ingests web search history for signed-in users, and identifies coherent contexts that correspond to tasks, interests, and habits. Unlike past work which focused on either in-session tasks or tasks over a few days, we look at several months of history in order to identify not just short-term tasks, but also long-term interests and habits. The features we use for identifying coherent contexts yield substantially higher precision and recall than past work. We also present an algorithm for identifying contexts that is 8 to 30 times faster than previous algorithms. The user modeling system has been deployed in production. It runs over hundreds of millions of users, and updates the models with a 10-minute latency. The contexts identified by the system serve as the foundation for generating recommendations in Google Now.

The paper considers how research in product/process design methodologies can be used to design engineering curricula. Following the Function-Behavior-Structure (FBS) framework for product design, we show how the curriculum design task can be separated into functionality of the curriculum, student behaviors and course structures. We then describe possible ways in which such a design could help us explore the possibilities of designing different types of curricula with the same functionality so that we could develop flexible, fault tolerant, scalable curricula that evolves with changing circumstances. These requirements are not generally included as curriculum design criteria and an approach that highlights the role of “robustness” would be useful.

An increasing fraction of the global discourse is migrating online in the form of blogs, bulletin boards, web pages, wikis, editorials, and a dizzying array of new collaborative technologies. The migration has now proceeded to the point that topics reflecting certain individual products are sufficiently popular to allow targeted online tracking of the ebb and flow of chatter around these topics. Based on an analysis of around half a million sales rank values for 2,340 books over a period of four months, and correlating postings in blogs, media, and web pages, we are able to draw several interesting conclusions.First, carefully hand-crafted queries produce matching postings whose volume predicts sales ranks. Second, these queries can be automatically generated in many cases. And third, even though sales rank motion might be difficult to predict in general, algorithmic predictors can use online postings to successfully predict spikes in sales rank.

We develop an algorithmic framework to decompose a collection of time-stamped text documents into semantically coherent threads. Our formulation leads to a graph decomposition problem on directed acyclic graphs, for which we obtain three algorithms --- an exact algorithm that is based on minimum cost flow and two more efficient algorithms based on maximum matching and dynamic programming that solve specific versions of the graph decomposition problem. Applications of our algorithms include superior summarization of news search results, improved browsing paradigms for large collections of text-intensive corpora, and integration of time-stamped documents from a variety of sources. Experimental results based on over 250,000 news articles from a major newspaper over a period of four years demonstrate that our algorithms efficiently identify robust threads of varying lengths and time-spans.

We introduce the Multi-Structural Database, a new data framework to support efficient analysis of large, complex data sets. An instance of the model consists of a set of data objects, together with a schema that specifies segmentations of the set of data objects according to multiple distinct criteria (e.g., into a taxonomy based on a hierarchical attribute). Within this model, we develop a rich set of analytical operations and design highly efficient algorithms for these operations. Our operations are formulated as optimization problems, and allow the user to analyze the underlying data in terms of the allowed segmentations.

We study the dynamics of information propagation in environments of low-overhead personal publishing, using a large collection of weblogs over time as our example domain. We characterize and model this collection at two levels. First, we present a macroscopic characterization of topic propagation through our corpus, formalizing the notion of long-running "chatter" topics consisting recursively of "spike" topics generated by outside world events, or more rarely, by resonances within the community. Second, we present a microscopic characterization of propagation from individual to individual, drawing on the theory of infectious diseases to model the flow. We propose, validate, and employ an algorithm to induce the underlying propagation network from a sequence of posts, and report on the results.

A (directed) network of people connected by ratings or trust scores, and a model for propagating those trust scores, is a fundamental building block in many of today's most successful e-commerce and recommendation systems. We develop a framework of trust propagation schemes, each of which may be appropriate in certain circumstances, and evaluate the schemes on a large trust network consisting of 800K trust scores expressed among 130K people. We show that a small number of expressed trusts/distrust per individual allows us to predict trust between any two people in the system with high accuracy. Our work appears to be the first to incorporate distrust in a computational trust propagation setting.

This paper describes Seeker, a platform for large-scale text analytics, and SemTag, an application written on the platform to perform automated semantic tagging of large corpora. We apply SemTag to a collection of approximately 264 million web pages, and generate approximately 434 million automatically disambiguated semantic tags, published to the web as a label bureau providing metadata regarding the 434 million annotations. To our knowledge, this is the largest scale semantic tagging effort to date.We describe the Seeker platform, discuss the architecture of the SemTag application, describe a new disambiguation algorithm specialized to support ontological disambiguation of large-scale data, evaluate the algorithm, and present our final results with information about acquiring and making use of the semantic tags. We argue that automated large scale semantic tagging of ambiguous content can bootstrap and accelerate the creation of the semantic web.

Activities such as Web Services and the Semantic Web are working to create a web of distributed machine understandable data. In this paper we present an application called 'Semantic Search' which is built on these supporting technologies and is designed to improve traditional web searching. We provide an overview of TAP, the application framework upon which the Semantic Search is built. We describe two implemented Semantic Search systems which, based on the denotation of the search query, augment traditional search results with relevant data aggregated from distributed sources. We also discuss some general issues related to searching and the Semantic Web and outline how an understanding of the semantics of the search terms can be used to provide better results.