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

Structured Transaction Definition Language (STDL) is a block-structured language specialized for distributed transaction processing, developed by the Multivendor Integration Architecture consortium. The main design goals were application portability across multiple interoperable STDL implementations, and ease of implementation on different vendors’ transaction processing systems both new and existing ones. This paper describes STDL’s transaction features: demarcating transaction boundaries, transactional remote procedure call, transactional queuing, recoverable terminal I/O, and transactional exception handling. STDL relies on standard C and COBOL for most application logic and all operations on files and SQL databases. All transactional features of STDL and new features outside standard C and COBOL are isolated in procedures written in the STDL task definition language. These features include demarcating transaction boundaries, transaction recovery, exception handling, transactional communications, access to data queues, submission of queued work requests, and invocation of presentation services. This isolation of transactional features is quite different than other persistent programming languages and has two important benefits: one can use applications written in standard C or COBOL, and to implement STDL, it is possible to map clauses of the task definition language onto operations of most any distributed TE monitor. ’ Address: Digital Equipment Corp., One Kendall Square, Building 700, Cambridge, MA 82139. Internet: pbemstein@crl.dec.com. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee andlor special permission from the Endowment. Proceedings of the 19th VLDB Conference Dublin, Ireland, 1993

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STDL - A Portable Language for Transaction Processing

Developers need to programmatically access persistent XML data. Object-oriented access is often the preferred method. Translating XML data into objects or vice-versa is a hard problem due to the data model mismatch and the difficulty of query translation. We propose a framework that addresses this problem by transforming object-based queries and updates into queries and updates on XML using flexible, declarative mappings between classes and XML schema types. The same mappings are used to shred XML fragments from query results into client-side objects. Information in the XML store that is not mapped using the mapping language, such as comments and processing instructions, are also made available in the object representation.

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Full-fidelity flexible object-oriented XML access

In optimistic concurrency control, a certifier algorithm processes a log of transaction operations to determine whether each transaction satisfies a given isolation level and therefore should commit or abort. This logging and certification of transactions is often sequential and can become a bottleneck. To improve transaction throughput, it is beneficial to parallelize or scale out the certifier and the log. One common technique for such parallelization is to partition the database. If the database is perfectly partitioned such that transactions only access data from a single partition, then both the log and the certifier can be parallelized such that each partition has its own independent log and certifier. However, for many applications, partitioning is only approximate, i.e., a transaction can access multiple partitions. Parallelization using such approximate partitioning requires synchronization between the certifiers and logs to ensure correctness. In this paper, we present the design of a parallel certifier and a partitioned log that uses minimal synchronization to obtain the benefits of parallelization using approximate partitioning. Our parallel certifier algorithm dynamically assigns constraints to each certifier. Certifiers enforce constraints using only atomic writes and reads on shared variables, thus avoiding expensive synchronization primitives such as locks. Our partitioned log uses a lightweight causal messaging protocol to ensure that transactions accessing the same partition appear in the same relative order in all logs where they both appear. We describe the techniques applied to an abstract certifier algorithm and log protocol, making them applicable to a variety of systems. We also show how both techniques can be used in Hyder, a scale-out log-structured indexed record manager.

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Scaling Optimistic Concurrency Control by Approximately Partitioning the Certifier and Log.

Model management is an approach to simplify the programming of metadata-intensive applications. It offers developers powerful operators, such as Compose, Extract, and Merge, that are applied to models, such as database schemas or interface specifications, and to mappings between models. To be used in practice, these operators need to be implemented for particular schema definition languages and mapping languages. To guide that implementation, we need a language-independent semantics that tells what the operators should do. In this paper we develop a state-based semantics of the operators. That is, we express the effect of applying the operators to models in terms of what the operators do to instances of these models. We show that our semantics captures previouslyproposed desiderata for the operators. We study formal properties of the operators, such as commutativity, associativity, uniqueness of results, and how the cardinality of the results corresponds to that of the inputs. Finally, we specify the statebased semantics of the operators in Rondo, the first prototype model-management system.

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A Semantics for Model Management Operators

The goal of data integration is to provide a uniform interface, called a mediated schema, to a set of autonomous data sources, which allows users to query a set of databases without knowing the schemas of the underlying data sources. 
This thesis describes two aspects of data integration: an algorithm for answering queries posed to a mediated schema and the process of creating a mediated schema. First, we present the MiniCon algorithm for answering queries in a data integration system and explain why MiniCon outperforms previous algorithms by up to several orders of magnitude. 
Second, given two relational schemas for data sources, we propose an approach for using conjunctive queries to describe mappings between them. We analyze their formal semantics, show how to derive a mediated schema based on such mappings, and show how to translate user queries over the mediated schema into queries over local schemas. We then show a generic Merge operator that merges schemas and mappings regardless of data model or application. Finally, we show how to implement the derivation of mediated schemas using the generic Merge operator.

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Philip A. Bernstein

Papers

STDL - A Portable Language for Transaction Processing

Full-fidelity flexible object-oriented XML access

Scaling Optimistic Concurrency Control by Approximately Partitioning the Certifier and Log.

A Semantics for Model Management Operators

Processing queries and merging schemas in support of data integration