Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

DBpedia is a community effort to extract structured information from Wikipedia and to make this information available on the Web. DBpedia allows you to ask sophisticated queries against datasets derived from Wikipedia and to link other datasets on the Web to Wikipedia data. We describe the extraction of the DBpedia datasets, and how the resulting information is published on the Web for human-andmachine-consumption. We describe some emerging applications from the DBpedia community and show how website authors can facilitate DBpedia content within their sites. Finally, we present the current status of interlinking DBpedia with other open datasets on the Web and outline how DBpedia could serve as a nucleus for an emerging Web of open data.

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DBpedia: a nucleus for a web of open data

Reliability at massive scale is one of the biggest challenges we face at Amazon.com, one of the largest e-commerce operations in the world; even the slightest outage has significant financial consequences and impacts customer trust. The Amazon.com platform, which provides services for many web sites worldwide, is implemented on top of an infrastructure of tens of thousands of servers and network components located in many datacenters around the world. At this scale, small and large components fail continuously and the way persistent state is managed in the face of these failures drives the reliability and scalability of the software systems.This paper presents the design and implementation of Dynamo, a highly available key-value storage system that some of Amazon's core services use to provide an "always-on" experience. To achieve this level of availability, Dynamo sacrifices consistency under certain failure scenarios. It makes extensive use of object versioning and application-assisted conflict resolution in a manner that provides a novel interface for developers to use.

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Dynamo: amazon's highly available key-value store

In Distributed Algorithms, Nancy Lynch provides a blueprint for designing, implementing, and analyzing distributed algorithms. She directs her book at a wide audience, including students, programmers, system designers, and researchers.



Distributed Algorithms contains the most significant algorithms and impossibility results in the area, all in a simple automata-theoretic setting. The algorithms are proved correct, and their complexity is analyzed according to precisely defined complexity measures. The problems covered include resource allocation, communication, consensus among distributed processes, data consistency, deadlock detection, leader election, global snapshots, and many others.



The material is organized according to the system model-first by the timing model and then by the interprocess communication mechanism. The material on system models is isolated in separate chapters for easy reference.



The presentation is completely rigorous, yet is intuitive enough for immediate comprehension. This book familiarizes readers with important problems, algorithms, and impossibility results in the area: readers can then recognize the problems when they arise in practice, apply the algorithms to solve them, and use the impossibility results to determine whether problems are unsolvable. The book also provides readers with the basic mathematical tools for designing new algorithms and proving new impossibility results. In addition, it teaches readers how to reason carefully about distributed algorithms-to model them formally, devise precise specifications for their required behavior, prove their correctness, and evaluate their performance with realistic measures.


Table of Contents

1 Introduction 
2 Modelling I; Synchronous Network Model 
3 Leader Election in a Synchronous Ring 
4 Algorithms in General Synchronous Networks 
5 Distributed Consensus with Link Failures 
6 Distributed Consensus with Process Failures 
7 More Consensus Problems 
8 Modelling II: Asynchronous System Model 
9 Modelling III: Asynchronous Shared Memory Model 
10 Mutual Exclusion 
11 Resource Allocation 
12 Consensus 
13 Atomic Objects 
14 Modelling IV: Asynchronous Network Model 
15 Basic Asynchronous Network Algorithms 
16 Synchronizers 
17 Shared Memory versus Networks 
18 Logical Time 
19 Global Snapshots and Stable Properties 
20 Network Resource Allocation 
21 Asynchronous Networks with Process Failures 
22 Data Link Protocols 
23 Partially Synchronous System Models 
24 Mutual Exclusion with Partial Synchrony 
25 Consensus with Partial Synchrony

Distributed algorithms

Schema matching is a basic problem in many database application domains, such as data integration, E-business, data warehousing, and semantic query processing. In current implementations, schema matching is typically performed manually, which has significant limitations. On the other hand, previous research papers have proposed many techniques to achieve a partial automation of the match operation for specific application domains. We present a taxonomy that covers many of these existing approaches, and we describe the approaches in some detail. In particular, we distinguish between schema-level and instance-level, element-level and structure-level, and language-based and constraint-based matchers. Based on our classification we review some previous match implementations thereby indicating which part of the solution space they cover. We intend our taxonomy and review of past work to be useful when comparing different approaches to schema matching, when developing a new match algorithm, and when implementing a schema matching component.

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A survey of approaches to automatic schema matching

Architecture that maps document data (e.g., XML-extended markup language) into columns of one table, thereby avoiding schema normalization problems through special data storage. Moreover, an algorithm is described that can translate a query (e.g., in XPath (XML path language), a query language for navigating through document elements and attributes of an XML document) into a relational algebra query of the document column representation. Based on the characteristics of the new mapping, query rewriting rules are provided that optimize the relational algebra query by minimizing the number of joins. The mapping of XML documents to the table is based on a summary structure and a hierarchical labeling scheme (e.g., ordpath) to enable a high-fidelity representation. Annotations are employed on the summary structure nodes to assist in mapping XML elements and attributes to the table.

Mapping documents to a relational database table with a document position column

We present an overview of a tutorial on model management---an approach to solving data integration problems, such as data warehousing, e-commerce, object-to-relational mapping, schema evolution and enterprise information integration. Model management defines a small set of operations for manipulating schemas and mappings, such as Match, Compose, Inverse, and Merge. The long-term goal is to build generic implementations of the operations that can be applied to a wide variety of data integration problems.

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Model management and schema mappings: theory and practice

Whenever multiple users or programs access a data base concurrently, the problem of concurrency control arises. The problem is to synchronize concurrent interactions so that each reads consistent data from the data base, writes consistent data, and is ultimately processed to completion. In a distributed data base this problem is exacerbated because a concurrency control mechanism at one site cannot instantaneously know about interactions at other sites. No fewer than 30 papers on this topic have appeared to date. Our purpose is to survey this literature, concentrating on three approaches—locking, majority consensus, and SDD-1 protocols—which together subsume the bulk of the literature. **

Approaches to concurrency control in distributed data base systems

Orleans provides a straightforward approach to building distributed interactive applications for the Cloud, without having to learn complex programming patterns for handling concurrency, fault tolerance, and resource management Orleans was made available as open source in January 2015

Developing Cloud Services Using the Orleans Virtual Actor Model

In an interface-based binary object system capable of supporting multiple interfaces into objects created by class templates, a relationship is defined as a pair of complementary collections on two separate interfaces, each interface found on separate objects A link between objects is formed when an interface of one object lists an object supporting the related interface included in the object and vice versa The collections may be of objects supporting the related interfaces or may be of specific relationship objects leading to objects supporting the related interfaces The collections are potentially multi-valued entities that enable relationships to be established between objects according to one-to-one, many-to-one, and many-to-many architectures By implementing a specific relationship object, behavior may be imparted to the relationship itself thereby providing more robust system behavior Objects thus linked can be easily traversed so that convenient navigation models can be implemented allowing clients quick access to desired objects by navigating through relationships between objects

Philip A. Bernstein

Papers

Mapping documents to a relational database table with a document position column

Model management and schema mappings: theory and practice

Approaches to concurrency control in distributed data base systems

Developing Cloud Services Using the Orleans Virtual Actor Model

Establishing relationships between objects based on object interfaces