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

物件導向軟體之架構(Object-Oriented Software Construction)探討

Process mining in healthcare

Finding a good join order is crucial for query performance. In this paper, we introduce the Join Order Benchmark (JOB) and experimentally revisit the main components in the classic query optimizer architecture using a complex, real-world data set and realistic multi-join queries. We investigate the quality of industrial-strength cardinality estimators and find that all estimators routinely produce large errors. We further show that while estimates are essential for finding a good join order, query performance is unsatisfactory if the query engine relies too heavily on these estimates. Using another set of experiments that measure the impact of the cost model, we find that it has much less influence on query performance than the cardinality estimates. Finally, we investigate plan enumeration techniques comparing exhaustive dynamic programming with heuristic algorithms and find that exhaustive enumeration improves performance despite the sub-optimal cardinality estimates.

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How good are query optimizers, really?

Predictive business process monitoring methods exploit logs of completed cases of a process in order to make predictions about running cases thereof. Existing methods in this space are tailor-made for specific prediction tasks. Moreover, their relative accuracy is highly sensitive to the dataset at hand, thus requiring users to engage in trial-and-error and tuning when applying them in a specific setting. This paper investigates Long Short-Term Memory (LSTM) neural networks as an approach to build consistently accurate models for a wide range of predictive process monitoring tasks. First, we show that LSTMs outperform existing techniques to predict the next event of a running case and its timestamp. Next, we show how to use models for predicting the next task in order to predict the full continuation of a running case. Finally, we apply the same approach to predict the remaining time, and show that this approach outperforms existing tailor-made methods.

Predictive Business Process Monitoring with LSTM Neural Networks

Relational XQuery systems try to re-use mature relational data management infrastructures to create fast and scalable XML database technology. This paper describes the main features, key contributions, and lessons learned while implementing such a system. Its architecture consists of (i) a range-based encoding of XML documents into relational tables, (ii) a compilation technique that translates XQuery into a basic relational algebra, (iii) a restricted (order) property-aware peephole relational query optimization strategy, and (iv) a mapping from XML update statements into relational updates. Thus, this system implements all essential XML database functionalities (rather than a single feature) such that we can learn from the full consequences of our architectural decisions. While implementing this system, we had to extend the state-of-the-art with a number of new technical contributions, such as loop-lifted staircase join and efficient relational query evaluation strategies for XQuery theta-joins with existential semantics. These contributions as well as the architectural lessons learned are also deemed valuable for other relational back-end engines. The performance and scalability of the resulting system is evaluated on the XMark benchmark up to data sizes of 11GB. The performance section also provides an extensive benchmark comparison of all major XMark results published previously, which confirm that the goal of purely relational XQuery processing, namely speed and scalability, was met.

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MonetDB/XQuery: a fast XQuery processor powered by a relational engine

Relational query processors derive much of their effectiveness from the awareness of specific table properties like sort order, size, or absence of duplicate tuples. This text applies (and adapts) this successful principle to database-supported XML and XPath processing: the relational system is made tree aware, i.e., tree properties like subtree size, intersection of paths, inclusion or disjointness of subtrees are made explicit. We propose a local change to the database kernel, the staircase join, which encapsulates the necessary tree knowledge needed to improve XPath performance. Staircase join operates on an XML encoding which makes this knowledge available at the cost of simple integer operations (e.g., +, ≤ ). We finally report on quite promising experiments with a staircase join enhanced main-memory database kernel.

https://www.vldb.org/conf/2003/papers/S16P03.pdf

Staircase join: teach a relational DBMS to watch its (axis) steps

This article is a proposal for a database index structure, the XPath accelerator, that has been specifically designed to support the evaluation of XPath path expressions. As such, the index is capable to support all XPath axes (including ancestor, following, preceding-sibling, descendant-or-self, etc.). This feature lets the index stand out among related work on XML indexing structures which had a focus on the child and descendant axes only. The index has been designed with a close eye on the XPath semantics as well as the desire to engineer its internals so that it can be supported well by existing relational database query processing technology: the index (a) permits set-oriented (or, rather, sequence-oriented) path evaluation, and (b) can be implemented and queried using well-established relational index structures, notably B-trees and R-trees.We discuss the implementation of the XPath accelerator on top of different database backends and show that the index performs well on all levels of the memory hierarchy, including disk-based and main-memory based database systems.

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Accelerating XPath evaluation in any RDBMS

In data integration efforts, portal development in particular, much development time is devoted to entity resolution. Often advanced similarity measurement techniques are used to remove semantic duplicates or solve other semantic conflicts. It proves impossible, however, to automatically get rid of all semantic problems. An often-used rule of thumb states that about 90% of the development effort is devoted to semi-automatically resolving the remaining 10% hard cases. In an attempt to significantly decrease human effort at data integration time, we have proposed an approach that strives for a `good enough' initial integration which stores any remaining semantic uncertainty and conflicts in a probabilistic database. The remaining cases are to be resolved with user feedback during query time. The main contribution of this paper is an experimental investigation of the effects and sensitivity of rule definition, threshold tuning, and user feedback on the integration quality. We claim that our approach indeed reduces development effort--and not merely shifts the effort--by showing that setting rough safe thresholds and defining only a few rules suffices to produce a `good enough' initial integration that can be meaningfully used, and that user feedback is effective in gradually improving the integration quality.

Qualitative effects of knowledge rules and user feedback in probabilistic data integration

Relational query processors are probably the best understood (as well as the best engineered) query engines available today. Although carefully tuned to process instances of the relational model (tables of tuples), these processors can also provide a foundation for the evaluation of "alien" (non-relational) query languages: if a relational encoding of the alien data model and its associated query language is given, the RDBMS may act like a special-purpose processor for the new language.

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Maurice van Keulen

Papers

MonetDB/XQuery: a fast XQuery processor powered by a relational engine

Staircase join: teach a relational DBMS to watch its (axis) steps

Accelerating XPath evaluation in any RDBMS

Qualitative effects of knowledge rules and user feedback in probabilistic data integration

Pathfinder: XQuery---the relational way