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

Crowdsourcing systems like Amazon's Mechanical Turk have emerged as an effective large-scale human-powered platform for performing tasks in domains such as image classification, data entry, recommendation, and proofreading. Since workers are low-paid (a few cents per task) and tasks performed are monotonous, the answers obtained are noisy and hence unreliable. To obtain reliable estimates, it is essential to utilize appropriate inference algorithms (e.g. Majority voting) coupled with structured redundancy through task assignment. Our goal is to obtain the best possible trade-off between reliability and redundancy. In this paper, we consider a general probabilistic model for noisy observations for crowd-sourcing systems and pose the problem of minimizing the total price (i.e. redundancy) that must be paid to achieve a target overall reliability. Concretely, we show that it is possible to obtain an answer to each task correctly with probability 1-e as long as the redundancy per task is O((K/q) log (K/e)), where each task can have any of the $K$ distinct answers equally likely, q is the crowd-quality parameter that is defined through a probabilistic model. Further, effectively this is the best possible redundancy-accuracy trade-off any system design can achieve. Such a single-parameter crisp characterization of the (order-)optimal trade-off between redundancy and reliability has various useful operational consequences. Further, we analyze the robustness of our approach in the presence of adversarial workers and provide a bound on their influence on the redundancy-accuracy trade-off.Unlike recent prior work [GKM11, KOS11, KOS11], our result applies to non-binary (i.e. K>2) tasks. In effect, we utilize algorithms for binary tasks (with inhomogeneous error model unlike that in [GKM11, KOS11, KOS11]) as key subroutine to obtain answers for K-ary tasks. Technically, the algorithm is based on low-rank approximation of weighted adjacency matrix for a random regular bipartite graph, weighted according to the answers provided by the workers.

/pdf/efficient-crowdsourcing-for-multi-class-labeling-4cmy88wsgx.pdf

Efficient crowdsourcing for multi-class labeling

The phenomenon known as Web logging ("blogging") has helped realize an initial goal of the Web: to turn Web content consumers (i.e., end users) into Web content producers. As the Semantic Web unfolds, we feel there are two questions worth posing: (1) do blog entries have semantic structure that can be usefully captured and exploited? (2) is blogging a natural way to encourage growth of the Semantic Web? We explore empirical evidence for answering these questions in the affirmative and propose means to bring blogging into the mainstream of the Semantic Web, including ontologies that extend the RSS 1.0 specification and an XSL transform for handling RSS 0.9x/2.0 files. To demonstrate the validity of our approach we have constructed a semantic blogging environment based on Haystack. We argue that with tools such as Haystack, semantic blogging will be an important paradigm by which metadata authoring will occur in the future.

/pdf/what-would-it-mean-to-blog-on-the-semantic-web-1c5c5w02lf.pdf

What would it mean to blog on the semantic web

Precise connections between algebraic network coding and network flows are explained in this article. Our combinatorial formulations offer new insights, mathematical simplicity, and lead to a substantially tighter upper bound on the coding field size required for a given connection problem than that in R. Koetter et al.

Network coding from a network flow perspective

Realtime crowdsourcing research has demonstrated that it is possible to recruit paid crowds within seconds by managing a small, fast-reacting worker pool. Realtime crowds enable crowd-powered systems that respond at interactive speeds: for example, cameras, robots and instant opinion polls. So far, these techniques have mainly been proof-of-concept prototypes: research has not yet attempted to understand how they might work at large scale or optimize their cost/performance trade-offs. In this paper, we use queueing theory to analyze the retainer model for realtime crowdsourcing, in particular its expected wait time and cost to requesters. We provide an algorithm that allows requesters to minimize their cost subject to performance requirements. We then propose and analyze three techniques to improve performance: push notifications, shared retainer pools, and precruitment, which involves recalling retainer workers before a task actually arrives. An experimental validation finds that precruited workers begin a task 500 milliseconds after it is posted, delivering results below the one-second cognitive threshold for an end-user to stay in flow.

Analytic Methods for Optimizing Realtime Crowdsourcing

Analysis of Elmore delay in distributed RC tree structures shows the influence of both tree cost and tree radius on signal delay in VLSI interconnects We give new and efficient interconnection tree constructions that smoothly combine the minimum cost and the minimum radius objectives, by combining respectively optimal algorithms due to Prim (1957) and Dijkstra (1959) Previous "shallow-light" techniques are both less direct and less effective: in practice, our methods achieve uniformly superior cost-radius tradeoffs Timing simulations for a range of IC and MCM interconnect technologies show that our wirelength savings yield reduced signal delays when compared to shallow-light or standard minimum spanning tree and Steiner tree routing >

David R. Karger

Papers

Efficient crowdsourcing for multi-class labeling

What would it mean to blog on the semantic web

Network coding from a network flow perspective

Analytic Methods for Optimizing Realtime Crowdsourcing

Prim-Dijkstra tradeoffs for improved performance-driven routing tree design