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

Data Mining - Concepts and Techniques.

Social Network Analysis

We study the problem of representation learning in heterogeneous networks. Its unique challenges come from the existence of multiple types of nodes and links, which limit the feasibility of the conventional network embedding techniques. We develop two scalable representation learning models, namely metapath2vec and metapath2vec++. The metapath2vec model formalizes meta-path-based random walks to construct the heterogeneous neighborhood of a node and then leverages a heterogeneous skip-gram model to perform node embeddings. The metapath2vec++ model further enables the simultaneous modeling of structural and semantic correlations in heterogeneous networks. Extensive experiments show that metapath2vec and metapath2vec++ are able to not only outperform state-of-the-art embedding models in various heterogeneous network mining tasks, such as node classification, clustering, and similarity search, but also discern the structural and semantic correlations between diverse network objects.

https://dl.acm.org/doi/pdf/10.1145/3097983.3098036

metapath2vec: Scalable Representation Learning for Heterogeneous Networks

The Analysis of Time Series: An Introduction, 4th edn. By C. Chatfield. ISBN 0 412 31820 2. Chapman and Hall, London, 1989. 242 pp. £13.50.

The Analysis of Time Series: An Introduction

Many kinds of real-life data exhibit logical ordering among their data items and are thus sequential in nature. In recent years, the concept of Sequence OLAP (S-OLAP) has been proposed. The biggest distinguishing feature of SOLAP from traditional OLAP is that data sequences managed by an S-OLAP system are characterized by the subsequence/substring patterns they possess. An S-OLAP system thus supports pattern-based grouping and aggregation. Conceptually, an S-OLAP system maintains a sequence data cube which is composed of sequence cuboids. Each sequence cuboid presents the answer of a pattern-based aggregate (PBA) query. This paper focuses on the I/O aspects of evaluating PBA queries. We study the problems of joining plan selection and execution planning, which are the core issues in the design of I/O-efficient cuboid materialization algorithms. Through an empirical study, we show that our algorithms lead to a very I/O-efficient strategy for sequence cuboid materialization.

I/O-efficient algorithms for answering pattern-based aggregate queries in a sequence OLAP system

In the original article, the Table 1 was published with incorrect figures. The correct Table 1 is given below

/pdf/correction-a-survey-of-community-search-over-big-graphs-341tpp2gin.pdf

Correction: A survey of community search over big graphs

In this paper, we propose a Deep Reinforcement Learning (RL) framework for task arrangement, which is a critical problem for the success of crowdsourcing platforms. Previous works conduct the personalized recommendation of tasks to workers via supervised learning methods. However, the majority of them only consider the benefit of either workers or requesters independently. In addition, they do not consider the real dynamic environments (e.g., dynamic tasks, dynamic workers), so they may produce sub-optimal results. To address these issues, we utilize Deep Q-Network (DQN), an RL-based method combined with a neural network to estimate the expected long-term return of recommending a task. DQN inherently considers the immediate and the future rewards and can be updated quickly to deal with evolving data and dynamic changes. Furthermore, we design two DQNs that capture the benefit of both workers and requesters and maximize the profit of the platform. To learn value functions in DQN effectively, we also propose novel state representations, carefully design the computation of Q values, and predict transition probabilities and future states. Experiments on synthetic and real datasets demonstrate the superior performance of our framework.

An End-to-End Deep RL Framework for Task Arrangement in Crowdsourcing Platforms

In typical location-based services (LBS), moving objects (e.g., GPS-enabled mobile phones) report their locations through a wireless network. An LBS server can use the location information to answer various types of continuous queries. Due to hardware limitations, location data reported by the moving objects are often uncertain. In this paper, we study efficient methods for the execution of Continuous Possible Nearest Neighbor Query (CPoNNQ) that accesses imprecise location data. A CPoNNQ is a standing query (which is active during a period of time) such that, at any time point, all moving objects that have non-zero probabilities of being the nearest neighbor of a given query point are reported. To handle the continuous nature of a CPoNNQ, a simple solution is to require moving objects to continuously report their locations to the LBS server, which evaluates the query at every time step. To save communication bandwidth and mobile devices' batteries, we develop two filter-based protocols for CPoNNQ evaluation. Our protocols install "filter bounds" on moving objects, which suppress unnecessary location reporting and communication between the server and the moving objects. Through extensive experiments, we show that our protocols can effectively reduce communication costs while maintaining a high query quality.

A filter-based protocol for continuous queries over imprecise location data

Trajectory queries, which retrieve nearby objects for every point of a given route, can be used to identify alerts of potential threats along a vessel route, or monitor the adjacent rescuers to a travel path. However, the locations of these objects (e.g., threats, succours) may not be precisely obtained due to hardware limitations of measuring devices, as well as complex natures of the surroundings. For such data, we consider a common model, where the possible locations of an object are bounded by a closed region, called “imprecise region”. Ignoring or coarsely wrapping imprecision can render low query qualities, and cause undesirable consequences such as missing alerts of threats and poor response rescue time. Also, the query is quite time-consuming, since all points on the trajectory are considered. In this paper, we study how to efficiently evaluate trajectory queries over imprecise objects, by proposing a novel concept, u-bisector, which is an extension of bisector specified for imprecise data. Based on the u-bisector, we provide an efficient and versatile solution which supports different shapes of commonlyused imprecise regions (e.g., rectangles, circles, and line segments). Extensive experiments on real datasets show that our proposal achieves better efficiency, quality, and scalability than its competitors.

http://dbtr.cs.aau.dk/DBPublications/DBTR-33.pdf

Reynold Cheng

Papers

I/O-efficient algorithms for answering pattern-based aggregate queries in a sequence OLAP system

Correction: A survey of community search over big graphs

An End-to-End Deep RL Framework for Task Arrangement in Crowdsourcing Platforms

A filter-based protocol for continuous queries over imprecise location data

Trajectory Possible Nearest Neighbor Queries over Imprecise Location Data