Model repair — aligning process models to reality

Process mining techniques relate observed behavior (i.e., event logs) to modeled behavior (e.g., a BPMN model or a Petri net). Process models can be discovered from event logs and conformance checking techniques can be used to detect and diagnose differences between observed and modeled behavior. Existing process mining techniques can only uncover these differences, but the actual repair of the model is left to the user and is not supported. In this paper we investigate the problem of repairing a process model w.r.t. a log such that the resulting model can replay the log (i.e., conforms to it) and is as similar as possible to the original model. To solve the problem, we use an existing conformance checker that aligns the runs of the given process model to the traces in the log. Based on this information, we decompose the log into several sublogs of non-fitting subtraces. For each sublog, either a loop is discovered that can replay the sublog or a subprocess is derived that is then added to the original model at the appropriate location. The approach is implemented in the process mining toolkit ProM and has been validated on logs and models from several Dutch municipalities.     

An overview on fault diagnosis and nature-inspired optimal control of industrial process applications

Fault detection, isolation and optimal control have long been applied to industry. These techniques have proven various successful theoretical results and industrial applications. Fault diagnosis is considered as the merge of fault detection (that indicates if there is a fault) and fault isolation (that determines where the fault is), and it has important effects on the operation of complex dynamical systems specific to modern industry applications such as industrial electronics, business management systems, energy, and public sectors. Since the resources are always limited in real-world industrial applications, the solutions to optimally use them under various constraints are of high actuality. In this context, the optimal tuning of linear and nonlinear controllers is a systematic way to meet the performance specifications expressed as optimization problems that target the minimization of integral- or sum-type objective functions, where the tuning parameters of the controllers are the vector variables of the objective functions. The nature-inspired optimization algorithms give efficient solutions to such optimization problems. This paper presents an overview on recent developments in machine learning, data mining and evolving soft computing techniques for fault diagnosis and on nature-inspired optimal control. The generic theory is discussed along with illustrative industrial process applications that include a real liquid level control application, wind turbines and a nonlinear servo system. New research challenges with strong industrial impact are highlighted.     

A novel approach to adaptive relational association rule mining

The paper focuses on the adaptive relational association rule mining problem. Relational association rules represent a particular type of association rules which describe frequent relations that occur between the features characterizing the instances within a data set. We aim at re-mining an object set, previously mined, when the feature set characterizing the objects increases. An adaptive relational association rule method, based on the discovery of interesting relational association rules, is proposed. This method, called ARARM (Adaptive Relational Association Rule Mining) adapts the set of rules that was established by mining the data before the feature set changed, preserving the completeness. We aim to reach the result more efficiently than running the mining algorithm again from scratch on the feature-extended object set. Experiments testing the method's performance on several case studies are also reported. The obtained results highlight the efficiency of the ARARM method and confirm the potential of our proposal.     

Geothermal energy utilization trends from a technological paradigm perspective

The use of geothermal energy and its associated technologies has been increasing worldwide. However, there has been little paradigmatic research conducted in this area. This paper proposes a systematic methodology to research the development trends for the sustainable development of geothermal energy. A novel data analysis system was created to research the geothermal energy utilization trends, and a technological paradigm theory was adopted to explain the technological changes. A diffusion velocity model was used to simulate and forecast the geothermal power generation development in the diffusion phase. Simulation results showed that the development of installed capacity for geothermal generation had a strong inertia force along with the S-curve. Power generation from geothermal power sources reached a peak in 2008 and is estimated to be saturated by 2030. Geothermal energy technologies in hybrid power systems based on other renewable energy sources look to be more promising in the future.     

一种基于Spark的不确定数据集频繁模式挖掘算法

如何在海量不确定数据集中提高频繁模式挖掘性能是目前研究的热点.传统算法大多是以期望、概率或者权重等单一指标为数据项集支持度，在大数据背景下，同时考虑概率和权重支持度的算法难以兼顾其执行效率.为此，本文提出一种基于Spark的不确定数据集频繁模式挖掘算法（UWEFP），首先，为了同时兼顾数据项的概率和权重，计算一项集的最大概率权重值并进行剪枝；然后，为了减少对数据集的多次扫描，结合Spark框架的优点，设计了一种具有FP-tree特征的新颖的UWEFP-tree结构进行模式树的构建及挖掘；最后在Spark环境下，以UCI数据集进行实验验证.实验结果表明本文的方法在保证挖掘结果的同时，提高了效率.     

基于数据挖掘的OLAP分析技术及应用

数据仓库和数据挖掘技术是目前信息技术研究的热点问题之一,介绍了数据挖掘(data mining)、数据仓库(datawarehouse)及联机分析处理(OLAP)技术。并以零售业决策支持系统为例,介绍了该系统的体系结构、数据仓库的设计、系统实现关键技术及各子系统功能,着重论述了如何利用OLAP技术实现企业决策支持系统。     

USERS' TWO-DIMENSIONAL INTEREST AND ITS STORAGE METHOD

The users' interest can be mined from the web cache and can be used widely. The interest can be specialized by the two-tuple (term, weight) in the simple interest model, in which the association relations are not mined, and then the interest cannot be associated in expressing the users' interest. Based on analyzing the WWW cache model, this letter brings forward a two-dimensional interest model and gives the interrelated methods on how to store the two-dimensional interest model effectively.     

USERS’ TWO-DIMENSIONAL INTEREST AND ITS STORAGE METHOD

The users‘ interest can be mined from the web cache and can be used widely.The interest can be specialized by the two-tuple(term,weight) in the simple interest model,in which the association relations are not mined.and then interest cannot be associated in expressing the users‘interest.Based on analyzing the WWW cache model,this letter brings forward a twodimensional interest model and gives the interrelated methods on how to store the two-dimensional interest model effectively.     

Ranking Pages by Topology and Popularity within Web Sites

We compare two link analysis ranking methods of web pages in a site. The first, called Site Rank, is an adaptation of PageRank to the granularity of a web site and the second, called Popularity Rank, is based on the frequencies of user clicks on the outlinks in a page that are captured by navigation sessions of users through the web site. We ran experiments on artificially created web sites of different sizes and on two real data sets, employing the relative entropy to compare the distributions of the two ranking methods. For the real data sets we also employ a nonparametric measure, called Spearman's footrule, which we use to compare the top-ten web pages ranked by the two methods. Our main result is that the distributions of the Popularity Rank and Site Rank are surprisingly close to each other, implying that the topology of a web site is very instrumental in guiding users through the site. Thus, in practice, the Site Rank provides a reasonable first order approximation of the aggregate behaviour of users within a web site given by the Popularity Rank.     

合理开发利用钨矿资源 促进钨矿业可持续发展

简述了我国钨矿业概况、现有采选技术水平及钨矿加工利用率 ;从资源配置出发 ,阐述了钨矿业发展存在的几个主要问题 ,并且针对性地提出了可持续发展的几点建议。