Basics of qualitative research: Grounded theory procedures and techniques

A fundamental problem that confronts peer-to-peer applications is to efficiently locate the node that stores a particular data item. This paper presents Chord, a distributed lookup protocol that addresses this problem. Chord provides support for just one operation: given a key, it maps the key onto a node. Data location can be easily implemented on top of Chord by associating a key with each data item, and storing the key/data item pair at the node to which the key maps. Chord adapts efficiently as nodes join and leave the system, and can answer queries even if the system is continuously changing. Results from theoretical analysis, simulations, and experiments show that Chord is scalable, with communication cost and the state maintained by each node scaling logarithmically with the number of Chord nodes.

Chord: A scalable peer-to-peer lookup service for internet applications

The network structure of a hyperlinked environment can be a rich source of information about the content of the environment, provided we have effective means for understanding it. We develop a set of algorithmic tools for extracting information from the link structures of such environments, and report on experiments that demonstrate their effectiveness in a variety of context on the World Wide Web. The central issue we address within our framework is the distillation of broad search topics, through the discovery of “authorative” information sources on such topics. We propose and test an algorithmic formulation of the notion of authority, based on the relationship between a set of relevant authoritative pages and the set of “hub pages” that join them together in the link structure. Our formulation has connections to the eigenvectors of certain matrices associated with the link graph; these connections in turn motivate additional heuristrics for link-based analysis.

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Authoritative sources in a hyperlinked environment

Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

Machine Learning : A Probabilistic Perspective

This paper presents the design and evaluation of Pastry, a scalable, distributed object location and routing substrate for wide-area peer-to-peer ap- plications. Pastry performs application-level routing and object location in a po- tentially very large overlay network of nodes connected via the Internet. It can be used to support a variety of peer-to-peer applications, including global data storage, data sharing, group communication and naming. Each node in the Pastry network has a unique identifier (nodeId). When presented with a message and a key, a Pastry node efficiently routes the message to the node with a nodeId that is numerically closest to the key, among all currently live Pastry nodes. Each Pastry node keeps track of its immediate neighbors in the nodeId space, and notifies applications of new node arrivals, node failures and recoveries. Pastry takes into account network locality; it seeks to minimize the distance messages travel, according to a to scalar proximity metric like the number of IP routing hops. Pastry is completely decentralized, scalable, and self-organizing; it automatically adapts to the arrival, departure and failure of nodes. Experimental results obtained with a prototype implementation on an emulated network of up to 100,000 nodes confirm Pastry's scalability and efficiency, its ability to self-organize and adapt to node failures, and its good network locality properties.

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Pastry: Scalable, Decentralized Object Location, and Routing for Large-Scale Peer-to-Peer Systems

As more and more reusable structured data appears on the Web, casual users will want to take into their own hands the task of mashing up data rather than wait for mash-up sites to be built that address exactly their individually unique needs. In this paper, we present Potluck, a Web user interface that let's casual users-those without programming skills and data modeling expertise-mash up data themselves. Potluck is novel in its use of drag and drop for merging fields, its integration and extension of the faceted browsing paradigm for focusing on subsets of data to align, and its application of simultaneous editing for cleaning up data syntactically. Potluck also lets the user construct rich visualizations of data in-place as the user aligns and cleans up the data. This iterative process of integrating the data while constructing useful visualizations is desirable when the user is unfamiliar with the data at the beginning-a common case-and wishes to get immediate value out of the data without having to spend the overhead of completely and perfectly integrating the data first. A user study on Potluck indicated that it was usable and learnable, and elicited excitement from programmers who, even with their programming skills, previously had great difficulties performing data integration.

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Potluck: Data Mash-Up Tool for Casual Users

This paper presents improved approximation algorithms for the problem of multiprocessor scheduling under uncertainty, or SUU, in which the execution of each job may fail probabilistically. This problem is motivated by the increasing use of distributed computing to handle large, computationally intensive tasks. In the SUU problem we are given n unit-length jobs and m machines, a directed acyclic graph G of precedence constraints among jobs, and unrelated failure probabilities q_{ij} for each job j when executed on machine i for a single timestep. Our goal is to find a schedule that minimizes the expected makespan, which is the expected time at which all jobs complete. 
Lin and Rajaraman gave the first approximations for this NP-hard problem for the special cases of independent jobs, precedence constraints forming disjoint chains, and precedence constraints forming trees. In this paper, we present asymptotically better approximation algorithms. In particular, we give an O(loglog min(m,n))-approximation for independent jobs (improving on the previously best O(log n)-approximation). We also give an O(log(n+m) loglog min(m,n))-approximation algorithm for precedence constraints that form disjoint chains (improving on the previously best O(log(n)log(m)log(n+m)/loglog(n+m))-approximation by a (log n/loglog n)^2 factor when n = poly(m). Our algorithm for precedence constraints forming chains can also be used as a component for precedence constraints forming trees, yielding a similar improvement over the previously best algorithms for trees.

Improved Approximations for Multiprocessor Scheduling Under Uncertainty

We present a novel Bayesian topic model for learning discourse-level document structure. Our model leverages insights from discourse theory to constrain latent topic assignments in a way that reflects the underlying organization of document topics. We propose a global model in which both topic selection and ordering are biased to be similar across a collection of related documents. We show that this space of orderings can be effectively represented using a distribution over permutations called the Generalized Mallows Model. We apply our method to three complementary discourse-level tasks: cross-document alignment, document segmentation, and information ordering. Our experiments show that incorporating our permutation-based model in these applications yields substantial improvements in performance over previously proposed methods.

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Content Modeling Using Latent Permutations

We consider two connectivity problems on undirected graphs: maximum flow and minimum k-way cut. The maximum flow problem asks about the connectivity between two specified nodes. A traditional approach is to search for augmenting paths. We explore the possibility of restricting the set of edges in which we search for an augmenting path, so that we can find each flow path in sub-linear time. Consider an n-vertex, m-edge, undirected graph with maximum flow value v. We give two methods for finding augmenting paths in such a graph in amortized sub-linear time, based on the idea that undirected graphs have sparse subgraphs that capture connectivity information. The first method sparsifies unused edges by using a spanning forest. It is deterministic and takes O(npv) time per path on average. The second method sparsifies the entire residual graph by taking random samples of the edges. It takes ~ O(n) time per path on average. These results let us improve the O(mv) time bound of the classic augmenting path algorithm to O(m+ nv3=2) (deterministic) and ~ O(m+ nv) (randomized). For simple graphs, the addition of a blocking flow subroutine yields a deterministic O(nm2=3v1=6)-time algorithm. A minimum k-way cut of an n-vertex,m-edge, weighted, undirected graph is a partition of the vertices into k sets that minimizes the total weight of edges with endpoints in different sets. We give new randomized algorithms to find minimum 3-way and 4-way cuts, which lead to time bounds of O(mnk-2 log3 n) for k 6. Our key insight is that two different structural properties of k-way cuts, both exploited by previous algorithms, can be exploited simultaneously to avoid the bottleneck operations in both prior algorithms. The result is that we improve on the best previous time bounds by a factor of ~ (n2). Thesis Supervisor: David R. Karger Title: Associate Professor of Electrical Engineering and Computer Science

Algorithms for connectivity problems in undirected graphs: maximum flow and minimum k-way cut

Conferences are ubiquitous in many industries, but how effectively they diffuse ideas has been debated and the mechanisms of diffusion unclear. Conference attendees can adopt ideas from presentations they choose to see (direct effect) or see incidentally (serendipity effect). We quantify these direct and serendipitous effects of academic conference presentations on future citations by exploiting quasi-random scheduling conflicts. When multiple papers of interest to an attendee are presented at the same time, the person is less able to see them on average and, if seeing presentations is important, cites them less. We use data from Confer, a scheduling application deployed at 25 in-person computer science conferences that lets users Like papers and receive personalized schedules. Compared to timeslots with many conflicts, users cited Liked papers with no scheduling conflicts 52% more. Users also cited non-Liked papers in sessions with no conflicts 51% more, and this serendipitous diffusion accounted for 22% of the overall diffusion induced by presentations. The study shows that conference presentations stimulate substantial direct and serendipitous diffusion of ideas, and adds the analysis of scheduling conflicts to the toolkit of management scholars.

David R. Karger

Papers

Potluck: Data Mash-Up Tool for Casual Users

Improved Approximations for Multiprocessor Scheduling Under Uncertainty

Content Modeling Using Latent Permutations

Algorithms for connectivity problems in undirected graphs: maximum flow and minimum k-way cut

The effect of conference presentations on the diffusion of ideas