Formal Modeling and Discovery of Multi-instance Business Processes: A Cloud Resource Management Case Study

Process discovery, as one of the most challenging process analysis techniques, aims to uncover business process models from event logs. Many process discovery approaches were invented in the past twenty years;however, most of them have difficulties in handling multi-instance sub-processes. To address this challenge, we first introduce a multi-instance business process model(MBPM) to support the modeling of processes with multiple sub-process instantiations. Formal semantics of MBPMs are precisely defined by using multi-instance Petri nets(MPNs)that are an extension of Petri nets with distinguishable tokens.Then, a novel process discovery technique is developed to support the discovery of MBPMs from event logs with sub-process multi-instantiation information. In addition, we propose to measure the quality of the discovered MBPMs against the input event logs by transforming an MBPM to a classical Petri net such that existing quality metrics, e.g., fitness and precision, can be used.The proposed discovery approach is properly implemented as plugins in the Pro M toolkit. Based on a cloud resource management case study, we compare our approach with the state-of-theart process discovery techniques. The results demonstrate that our approach outperforms existing approaches to discover process models with multi-instance sub-processes.

Resources publication and discovery in manufacturing grid

In the manufacturing grid＇s architecture, Resources Management System （RMS） is the central component responsible for disseminating resource information across the grid, accepting requests for resources, discovering and scheduling the suitable resources that match the requests for the global grid resource, and executing the requests on scheduled resources. In order to resolve the problem of resources publication and discovery in Manufacturing Grid （MGrid）, the classification of manufacturing resources is first researched after which the resources encapsulation class modes are put forward. Then, a scalable two-level resource management architecture is constructed on the model, which includes root nodes, domain nodes and leaf nodes. And then an RIMS is proposed, and the resources publication and discovery mechanism are detailedly described. At last, an application prototype is developed to show the validity and the practicability of the proved theory and method.

Automatic Discovery of Information Resources on the World Wide Web

A basis for automatic discovery of information resources on the World Wide Web is characterized by three underlying equations. With these equations, the information universe on the Web is divided into three associated spaces. This model differs from the hypertext employed by the Web, in that the former supports the notion of automatic resource discovery. A private library, which is able to gather automatically from the Web the information useful to the library owner, is envisaged to illustrate that the basic equations and their derivations can be applied to Web automation, including crawling and classifying.

Context-aware computing-based reducing cost of service method in resource discovery and interaction

Reducing cost of service is an important goal for resource discovery and interaction technologies. The shortcomings of transhipment-method and hibernation-method are to increase holistic cost of service and to slower resource discovery respectively. To overcome these shortcomings, a context-aware computing-based method is developed. This method, firstly, analyzes the courses of devices using resource discovery and interaction technologies to identify some types of context related to reducing cost of service, then, chooses effective methods such as stopping broadcast and hibernation to reduce cost of service according to information supplied by the context but not the transhipment-method’s simple hibernations. The results of experiments indicate that under the worst condition this method overcomes the shortcomings of transhipment-method, makes the “poor” devices hibernate longer than hibernation-method to reduce cost of service more effectively, and discovers resources faster than hibernation-method; under the best condition it is far better than hibernation-method in all aspects.

Relative Efficiency Evaluation on Water Resource Utilization

Water resource allocation was defined as an input-output question in this paper, and a preliminary input-output index system was set up. Then GEM （group eigenvalue method）-MAUE （multi-attribute utility theory） model was applied to evaluate relative efficiency of water resource allocation plans. This model determined weights of indicators by GEM, and assessed the allocation schemes by MAUE. Compared with DEA （Data Envelopment Analysis） or ANN （Artificial Neural Networks）, the mode was more applicable in some cases where decision-makers had preference for certain indicators

Analytical Comparison of Resource Search Algorithms in Non-DHT Mobile Peer-to-Peer Networks

One of the key challenges in ad-hoc networks is the resource discovery problem.How efciently&quickly the queried resource/object can be resolved in such a highly dynamic self-evolving network is the underlying question?Broadcasting is a basic technique in the Mobile Ad-hoc Networks(MANETs),and it refers to sending a packet from one node to every other node within the transmission range.Flooding is a type of broadcast where the received packet is retransmitted once by every node.The naive ooding technique oods the network with query messages,while the random walk scheme operates by contacting subsets of each node’s neighbors at every step,thereby restricting the search space.Many earlier works have mainly focused on the simulation-based analysis of ooding technique,and its variants,in a wired network scenario.Although,there have been some empirical studies in peer-to-peer(P2P)networks,the analytical results are still lacking,especially in the context of mobile P2P networks.In this article,we mathematically model different widely used existing search techniques,and compare with the proposed improved random walk method,a simple lightweight approach suitable for the non-DHT architecture.We provide analytical expressions to measure the performance of the different ooding-based search techniques,and our proposed technique.We analytically derive 3 relevant key performance measures,i.e.,the avg.number of steps needed to nd a resource,the probability of locating a resource,and the avg.number of messages generated during the entire search process.

Device Discovery in D2D Communication: Scenarios and Challenges

Device to Device (D2D) communication is expected to be anessential part of 5G cellular networks. D2D communication enables closeproximitydevices to establish a direct communication session. D2D communicationoffers many advantages, such as reduced latency, high data rates,range extension, and cellular offloading. The first step to establishing a D2Dsession is device discovery;an efficient device discovery will lead to efficientD2D communication. D2D device further needs to manage its mode of communication,perform resource allocation, manage its interference and mostimportantly control its power to improve the battery life of the device. Thiswork has developed six distinct scenarios in which D2D communication canbe initiated, considering their merits, demerits, limitations, and optimizationparameters. D2D communication procedures for the considered scenarioshave been formulated, based upon the signal flow, containing device discovery,resource allocation, and session teardown. Finally, latency for each scenariohas been evaluated, based on propagation and processing delays.

社区参与中的资源发掘路径探析——以F街社区互助会为例

社区居民有效参与的实现与呈现,相当依赖于可动用的社区资源。在我国社区治理中,普遍存在居民参与不力的现象或问题,且与参与资源匮乏密切相关。从资源视角出发,分析社区参与主体的资源相互依赖关系,探讨社区参与中的资源发掘路径,有助于破解社区居民参与困境。研究发现,拓展资源枢纽型组织的资源发掘路径、利用政府在社区资源配置中的主导性地位、建构多元化与多层次的社区资源供给体系,是缓解社区参与不足、最终走出社区参与困境的可行路径。

关联数据公共目录探究

近年来已有许多图书馆导入关联数据,提供多种在线公共目录的信息服务。文章依据关联数据公共目录的发展现况,选取具有丰富书目本体语义关系的简·奥斯汀的相关及其衍生作品为样本,同时以西班牙国家图书馆、法国国家图书馆,及美国国会图书馆的关联数据服务和共享型虚拟探索环境4个案例为对象进行评估,发现已具备关联化信息探索等10项功能,特点不尽相同,包括网络化公共目录、关联数据公共目录类型、关联数据化方式、URI关联化程度、语义关系与开放程度等。最后针对关联数据公共目录与资源探索系统在多个方面加以比较。

化学生物学课程实践性教学探索——以药物化学ChEMBL数据库应用为例

化学生物学作为一门新兴的交叉学科,在教学中需要引入当前先进的信息学内容。在课程的教学中常常涉及大量的化合物及药物的知识介绍,我们在教学中尝试引入药物化学信息ChEMBL数据库的内容到本科生化学生物学课程中。ChEMBL是一个开放数据库,包含大量药物化合物的结构、功能和药代动力学等信息。这些数据定期从主要发表的文献中提取,然后通过进一步的策划和标准化,以最大限度地提高它们在化学生物学和药物发现研究中的效用。将ChEMBL数据资源引入到化学生物学本科教学中,使学生能够利用网络数据对所学到的药物分子进行分析,取得了较好的教学效果。

基于概率主题的物品万维网资源管理机制

针对物品万维网(WoT)资源管理复杂度高、效率低的问题,提出一种基于概率主题模型的WoT资源智能化管理机制。从资源描述中自动提取主题信息;利用分类算法对该主题进行分类;根据分类结果将资源发布在WoT网关构建的P2P网络中。仿真实验表明,该方法与基于文本及语义的方法相比,具有较高的管理效率以及准确率,同时,基于相同主题的资源分布式存储也提高了应用的可用性。最后,给出一个WoT资源优化管理的实例分析。

基于关联数据的机构知识库实体学术关系发现方法研究

文章分析机构知识库实体学术关系类型及其特征属性,依托关联数据构建机构知识库实体学术关系发现方法并阐释其具体操作流程,利用河海大学机构知识库数据开展实证检验。结果表明,使用该方法可实现机构知识库资源语义聚集与关联,提供基于关联数据的知识检索服务,以精准满足用户知识需求。

一种面向大规模资源发现的分布式局部聚类方法

在大规模资源环境下,传统的资源索引机制导致Peer结点数量急剧增加和负载均衡性能下降,影响查询效率和系统稳定性。本文提出了一种质心模型的局部资源聚类方法,通过将相近资源聚类于单一结点并选出代表性键,有效减少了P2P(Peer-to-peer)网络中的Peer结点规模。此外,局部聚类机制集中处理距离相近的键,避免了资源覆盖的过度膨胀。实验结果显示,基于质心模型的Skip Graph算法不仅降低了查询复杂度,提高了负载均衡性能,而且在网络规模、数据量及查询复杂度方面展现出优秀的扩展性,更好地适应大规模资源发现的需求。

幼儿园科学发现室的价值与创设

幼儿园科学发现室是在幼儿园设立的专供幼儿进行非正规性科学活动的场所,其创设、利用与科学领域课程紧密相关。通过为幼儿提供探索、观察和操作的材料,使幼儿主动探索科学现象、发现初步的科学奥秘。幼儿园科学发现室是幼儿科学教育的重要支持场景,其环境的创设至关重要。应树立大课程观,充分考虑科学教育与其他各学科教育的联系,全面培养幼儿的科学素养,充分发挥科学发现室的科学启蒙教育作用。

基于遥感影像的生态环境和资源保护公益诉讼调查取证方法研究

针对生态环境和资源保护领域公益诉讼工作面临的线索发现难、调查取证难等现势需求,本研究提出了一种基于遥感影像的生态环境和资源保护公益诉讼调查取证方法。利用U-Net++模型进行变化检测,结合生态环境和资源保护领域中公益诉讼对目标受损现状、变化情况等相关事实认定的要求,对变化检测图斑进行筛选,作为公益诉讼案件调查的线索或依据;并与已有方法卷积神经网络、U-Net模型进行比较实验;同时,以贵州省检察机关为试点,研发面向公益诉讼的卫星遥感与无人机联动辅助取证平台。结果表明:本方法得到的检测准确度为83.08%,精确度为93.91%,相较其他两种模型在精确度、召回率、F1分数三个指标上的表现更好;本方法已在贵州省检察机关进行了应用实践。研究成果表明可为检察机关快速发现线索、还原事实真相提供技术参考。

鲁南新庄地区多金属矿产地质调查及成矿潜力分析

在实施山东省15万新庄幅区域矿产地质调查项目过程中,通过收集分析整理以往有关地质成果,利用遥感地质调查、物探工作(包括重力面积工作、磁测面积工作、重磁剖面工作、物性工作、地面γ能谱剖面测量)、化探重砂测量工作、采样及测试分析、矿产综合检查、综合调查研究等技术方法,圈定3处遥感异常和3处找矿靶区,圈定局部重力异常11处,其中重力高异常5处,重力低异常6处,并择优进行了查证,重砂测量在9条煌斑岩脉内挑选出大量金刚石指示矿物946颗,缩小了区内金刚石找矿范围,提供了金刚石矿产的找矿信息。本次矿产地质调查工作圈定1处典型成矿预测区(估算金矿金属量3101 kg,稀土氧化物总量50212 t)、2处成矿远景区、4处找矿预测区、1处找矿靶区,新发现铜锌金矿点1处、铜矿点1处、铜及稀土矿点1处、稀土矿点1处,对其成矿地质条件进行综合分析,找矿潜力作出评价。

On the Decision Structures and Knowledge Discovery for ANP Modeling

This paper proposes an integrative framework for network-structured analytic network process (ANP) modeling. The underlying rationales include: 1) creating the measuring items for the complex decision problems;2) applying factor analysis to reduce the complex measuring items into fewer constructs;3) employing Bayesian network classifier technique to discover the causal directions among constructs;4) using partial least squares path modeling to test the causal relationships among the items-constructs. The proposed framework is implemented for knowledge discovery to a case of high-tech companies’ enterprise resource planning (ERP) benefits and satisfaction in Hsinchu Science Park,Taiwan. The results show that the proposed framework for ANP modeling can reach a satisfactory level of convergent reliability and validity. Based on the findings, pragmatic implications to the ERP venders are discussed. This study has shed new light on the long neglected, yet critical, issue on decision structures and knowledge discovery for ANP modeling.

Knowledge Discovery in Data: A Case Study

It is common in industrial construction projects for data to be collected and discarded without being analyzed to extract useful knowledge. A proposed integrated methodology based on a five-step Knowledge Discovery in Data (KDD) model was developed to address this issue. The framework transfers existing multidimensional historical data from completed projects into useful knowledge for future projects. The model starts by understanding the problem domain, industrial construction projects. The second step is analyzing the problem data and its multiple dimensions. The target dataset is the labour resources data generated while managing industrial construction projects. The next step is developing the data collection model and prototype data ware-house. The data warehouse stores collected data in a ready-for-mining format and produces dynamic On Line Analytical Processing (OLAP) reports and graphs. Data was collected from a large western-Canadian structural steel fabricator to prove the applicability of the developed methodology. The proposed framework was applied to three different case studies to validate the applicability of the developed framework to real projects data.

数据与技术双轮驱动的生物医学信息学发展与展望

【目的】生物医学信息学是发展最快的代表性专业信息学之一。梳理生物医学信息学理论体系与最新进展,展望学科未来发展路径尤为重要。【方法】通过文献计量与调研,我们分析了生物医学信息学主要研究内容,并对数据资源体系、数据分析算法与模型、数据分析工具与软件等方面的最新进展进行了详细梳理。【结果】该领域逐渐形成了以大数据资源为基础、以技术为手段、以人才为核心、以应用为导向的学科发展路径,预计这一新的范式将有效支撑生物医学信息学的高速、深入发展。【局限】基于篇幅,一些生物医学数据资源与方法等的分析广度有限。【结论】数据与技术是生物医学信息学发展的两个关键环节,通过本文的梳理与展望,以期为我国生物医学信息学学科完善与发展提供参考。

基于知识组织的诊疗资源融合与发现平台设计及实现

目的/意义解决诊疗资源类型有限、揭示深度不足、融合关联度不高等问题,为多源异构诊疗资源的语义融合与发现奠定基础。方法/过程提出基于知识体系的资源汇聚、基于增强表示的数据关联、基于实体对齐的内容整合等融合方法,实现专题型资源、文献型资源、公众健康教育资源的深度规范描述、语义融合与关联。结果/结论研发诊疗资源融合与发现平台,通过智能检索与推荐、结果聚类与分面分级、语义关联等服务,支撑诊疗资源的多维度融合与发现。