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.

/pdf/authoritative-sources-in-a-hyperlinked-environment-1p23r698i9.pdf

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.

/pdf/pastry-scalable-decentralized-object-location-and-routing-1u2equbbbp.pdf

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

The Canadian T raveller problem is a stochastic shortest paths problem in which one learns the costof an edge only when arriving at one of its endpoints. The goal is to nd an adapti ve polic y (adjustingas one learns more edge lengths) that minimizes the expected cost of travel. The problem is kno wn to be#P hard. Since there has been no signicant progress on approximation algorithms for several decades,we ha ve chosen to seek out special cases for which exact solutions exist, in the hope of demonstratingtechniques that could lead to further progress. Applying techniques from the theory of Mark ov DecisionProcesses, we give an exact solution for graphs of parallel (undirected) paths from source to destinationwith random tw o-v alued edge costs. W e also offer a partial generalization to traversing perfect binarytrees. MIT Computer Science & AI Lab, Cambridge MA 02139, USA, kar ger@csail.mit.eduy MIT Computer Science & AI Lab, Cambridge MA 02139, USA, enik olo va@csail.mit.edu

/pdf/exact-algorithms-for-the-canadian-traveller-problem-on-paths-2ueipfxlr2.pdf

Exact Algorithms for the Canadian Traveller Problem on Paths and Trees

While the web contains many social websites, people are generally left in the dark about the activities of other people traversing the web as a whole. In this paper, we explore the potential benefits and privacy considerations around generating a real-time, publicly accessible stream of web activity where users can publish chosen parts of their web browsing data. Taking inspiration from social media systems, we describe individual benefits that can be unlocked by such sharing and that may incentivize users to publish aspects of their browsing. We ask whether and how these benefits outweigh potential costs in lost privacy. We conduct our study of public web activity sharing through scenario-based interviews and a field deployment of a tool for web activity sharing.

/pdf/opportunities-and-challenges-around-a-tool-for-social-and-223ubzo14f.pdf

Opportunities and Challenges Around a Tool for Social and Public Web Activity Tracking

In this paper, we describe a system that can extract recordstructures from web pages with no direct human supervision.Records are commonly occurring HTML-embedded data tuples that describe people, offered courses, products,company profiles, etc. We present a simplified frameworkfor studying the problem of unsupervised record extraction. one which separates the algorithms from the feature engineering.Our system, U-REST formalizes an approach tothe problem of unsupervised record extraction using a simple two-stage machine learning framework. The first stage involves clustering, where structurally similar regions are discovered, and the second stage involves classification, where discovered groupings (clusters of regions) are ranked by their likelihood of being records. In our work, we describe, and summarize the results of an extensive survey of features for both stages. We conclude by comparing U-REST to related systems. The results of our empirical evaluation show encouraging improvements in extraction accuracy.

/pdf/u-rest-an-unsupervised-record-extraction-system-4d1zek2cph.pdf

U-REST: an unsupervised record extraction system

Recently, Aissi et al. gave new counting and algorithmic bounds for parametric minimum cuts in a graph, where each edge cost is a linear combination of multiple cost criteria and different cuts become minimum as the coefficients of the linear combination are varied. In this article, we derive better bounds using a mathematically simpler argument. We provide faster algorithms for enumerating these cuts. We give a lower bound showing our upper bounds have roughly the right degree. Our results also immediately generalize to parametric versions of other problems solved by the Contraction Algorithm, including approximate min-cuts, multi-way cuts, and a matroid optimization problem. We also give a first generalization to nonlinear parametric minimum cuts.

https://dl.acm.org/doi/pdf/10.1145/2897518.2897578

Enumerating parametric global minimum cuts by random interleaving

One of the primary goals of academic conferences is to promote scientific exchange of advances among people who may otherwise not have the opportunity to hear from one another. We present Confer, a tool designed to help conference attendees find interesting papers and talks, dis-cover and meet people with shared interests, and manage their time using a personalized schedule for the conference. So far, we have deployed Confer to 17 academic confer-ences including several years of CHI and CSCW. Confer is also the primary program tool for CSCW 2016. Log analysis and survey results have shown that the tool helps confer-ence attendees find interesting papers and manage their schedule. Furthermore the recommendation data gener-ated from the tool has been used by conference organizers to help plan conference schedules as well as to organize social gatherings. Finally, the meetup feature seeks to help conference attendees reach out to other attendees with similar interests.

/pdf/confer-a-conference-recommendation-and-meetup-tool-3b4vjm31fr.pdf

David R. Karger

Papers

Exact Algorithms for the Canadian Traveller Problem on Paths and Trees

Opportunities and Challenges Around a Tool for Social and Public Web Activity Tracking

U-REST: an unsupervised record extraction system

Enumerating parametric global minimum cuts by random interleaving

Confer: A Conference Recommendation and Meetup Tool