工作流参考模型研究与基于J2EE实现

实施工作流管理的主要目的是通过合理地调用和分配与活动有关的信息和资源来实现企业业务处理的高效、自动化执行.本文基于J2EE规范和工作流管理联盟规范,研究了抽象的工作流参考模型的功能组成部件、工作流执行服务、工作流执行引擎、接口以及接口间的信息交换,提出了一种由工作流引擎驱动的基于J2EE的框架实现,在此基础之上,工作流管理系统能很好地实现将企业业务处理过程中活动、信息及资源的调度策略集成到业务的自动协调操作中来.     

Preventing Timing Leaks Through Transactional Branching Instructions

Timing channels constitute one form of covert channels through which programs may be leaking information about the confidential data they manipulate. Such timing channels are typically eliminated by design, employing ad-hoc techniques to avoid information leaks through execution time, or by program transformation techniques, that transform programs that satisfy some form of noninterference property into programs that are time-sensitive termination-sensitive non-interfering. However, existing program transformations are thus far confined to simple languages without objects nor exceptions.We introduce a program transformation that uses transaction mechanisms to prevent timing leaks in sequential object-oriented programs. Under some strong but reasonable hypotheses, the transformation preserves the semantics of programs and yields for every termination-sensitive noninterfering program a time-sensitive termination-sensitive non-interfering program.     

„Deal of the Day“-Plattformen: Was treibt die Kundenloyalität?

“Deal of the Day” (DoD) platforms have quickly become popular by offering savings on local services, products and vacations. For merchants, these platforms represent a new marketing channel to advertise their products and services and attract new customers. DoD platform providers, however, struggle to maintaining a stable market share and profitability, because entry and switching costs are low. To sustain a competitive market position, DoD providers are looking for ways to build a loyal customer base. However, research examining the determinants of user loyalty in this novel context is scarce. To fill this gap, this study employs Grounded Theory methodology to develop a conceptual model of customer loyalty to a DoD provider. In the next step, qualitative insights are enriched and validated using quantitative data obtained in a survey with 202 DoD users. We find that customer loyalty is in large part driven by monetary incentives, but can be eroded if impressions from merchant encounter are below expectations. In addition, enhancing the share of deals relevant for consumers, i.e. signal-to-noise ratio, and mitigating perceived risks of a transaction emerge as challenges. Beyond theoretical value, our results offer practical insights into how customer loyalty to a DoD provider can be promoted.     

Exploring the online bidder’s repurchase intention: a cost and benefit perspective

The success of online auctions is founded on bidders enjoying shopping benefits and on creating bidders’ loyalty. This study investigates the importance of bidders’ repurchase intention along with the corresponding cost and benefit aspects. Therefore, this study integrates transaction cost economics and expectancy confirmation theory to understand the determinants of bidders’ repurchase intention in online auctions. We collected data from a survey questionnaire, and a total of 594 valid questionnaires were analyzed. Partial least squares structural equation modeling was used to assess the relationships of the research model. The findings show that satisfaction has a significant influence on bidders’ repurchase intention, while transaction cost is negatively associated with repurchase intention. Bidders’ satisfaction is determined by confirmation and by the e-service quality of both auctioneers and sellers. Moreover, an auctioneer’s asset specificity and product uncertainty are positively associated with the bidder’s perceived transaction cost. The interaction frequency between bidder and seller is negatively associated with the bidder’s transaction costs. The research results provide a novel approach to understanding bidders’ benefit and cost dimensions in online auction marketplaces. Our findings could guide online auctioneers and sellers in enhancing their offerings.     

基于交易网络的公有链用户识别方法

分析与研究公有链交易数据及系统用户行为对于保证公有链应用安全至关重要。比特币作为公有链的代表性应用,是一种基于P2P网络的电子现金系统。比特币交易地址具有匿名特征,无法关联到用户真实信息,这使得比特币溯源非常困难。为识别比特币中交易地址间的关联关系,推断出用户真实信息,提出一种基于交易网络的用户识别方法。对比特币区块数据进行预处理,通过解析比特币区块数据中的脚本信息,将比特币原始交易数据处理为更加直观的数据格式。衡量交易输入与输出地址间的相似程度,根据交易地址关联信息识别出比特币匿名地址对应的所有用户。在实验中应用真实的比特币区块数据,利用可视化方式对用户识别结果进行分析,结果表明该方法不受交易规则的限制,能对比特币匿名地址进行有效识别,且随着比特币区块数量的增加,识别准确率基本稳定于80%。     

In whose bed shall I sleep tonight? The impact of transaction-specific versus partner-specific information on pricing on a sharing platform

Platform providers in the sharing economy such as AirBnB face a trade-off between providing information on the object of the transaction and providing more information about the transaction partner. We view this trade-off from an economic perspective as the choice between complete or relational contracts. Using a global data set of AirBnB listings, we empirically compare the strength of influence of premises-specific versus host-specific information on pricing on that platform. The results indicate that partner-specific information in fact has only a comparatively weak influence on prices. This sheds critical light on the frequently used argument that in the sharing economy, personal information is essential to establish the necessary trust between transaction partners. We also find that the importance of premises-specific versus partner-specific information varies with the type of premises being rented.     

一种用于存储与查询半结构化数据的新方法

由于半结构化数据缺乏模式信息,因而半结构化数据的存储与查询将是一个十分重要且具有挑战性的研究课题。利用关系数据库存储半结构化数据可以重用数据库的查询优化器和事务处理机制,能够保证半结构化数据的一致性和完整性。该文提出一种实现半结构化数据存储与查询的新方法,该方法使用关系数据库系统来实现半结构化数据的存储与查询。给出了把基于半结构化数据的查询重写为基于关系的查询的算法,同时介绍一个可视化查询程序。     

Construct the stable vendor managed inventory partnership through a profit-sharing approach

In real life, the vendor managed inventory (VMI) model is not always a stable supply chain partnership. This paper proposes a cooperative game based profit-sharing method to stabilize the VMI partnership. Specifically, in a B2C setting, we consider a VMI program including a manufacturer and multiple online retailers. The manufacturer provides the finished product at the equal wholesale price to multiple online retailers. The online retailers face the same customer demand information. We offer the model to compute the increased profits generated by information sharing for total possible VMI coalitions. Using the solution concept of Shapley value, the profit-sharing scheme is produced to fairly divide the total increased profits among the VMI members. We find that under a fair allocation scheme, the higher inventory cost of one VMI member increases the surplus of the other members. Furthermore, the manufacturer is glad to increase the size of VMI coalition, whereas, the retailers are delighted to limit the size of the alliance. Finally, the manufacturer can select the appropriate retailer to boost its surplus, which has no effect on the surplus of the other retailers. The numerical examples indicate that the grand coalition is stable under the proposed allocation scheme.     

Rebalancing an investment portfolio in the presence of convex transaction costs,including market impact costs

The inclusion of transaction costs is an essential element of any realistic portfolio optimization. We extend the standard portfolio optimization problem to consider convex transaction costs incurred when rebalancing an investment portfolio. Market impact costs measure the effect on the price of a security that result from an effort to buy or sell the security, and they can constitute a large part of the total transaction costs. The loss to a portfolio from market impact costs is often modelled with a convex function that can be expressed using second-order cone constraints. The Markowitz framework of mean-variance efficiency is used. In order to properly represent the variance of the resulting portfolio, we suggest rescaling by the funds available after paying the transaction costs. This results in a fractional programming problem, which we show can be reformulated as an equivalent convex program of size comparable to the model without transaction costs. We show that an optimal solution to the convex program can always be found that does not discard assets.     

A framework to identify factors affecting the performance of third-party B2B e-marketplaces: A seller’s perspective

This study conducted exploratory research to determine factors affecting the performance of third-party B2B e-marketplaces from a seller’s perspective. Building on the relevant literature, ten factors were proposed and mapped into the domain-specific model for e-marketplaces. The performance of e-marketplaces was measured in terms of customer loyalty and trading volume. The proposed model was tested on data from 200 selling companies in Thailand participating in various third-party B2B e-marketplaces. The exploratory factor analysis generated seven factors. Partial least squares structural equation modeling was employed to test the research model. There were three major findings. First, the reputation of e-marketplace and trust in market makers, as well as transaction cost reduction and website usability, had significant effects on customer loyalty. Second, website reliability had a positive impact on trading volume. Finally, the relative advantage and number of buyers had significant effects on both customer loyalty and trading volume. These findings reveal factors previously unreported in the literature on e-marketplaces, and they can be used by practitioners to improve performance.     

Consumer behavior in online game communities: A motivational factor perspective

The concept of online communities has been used to improve customers’ loyalty in recent years. While studies on transaction community such as online auction have received more attention in the literature, entertainment community such as online game has seldom been addressed. This study applies the theory of reasoned action (TRA) and modifies the technology acceptance model (TAM) to propose a research model. An empirical study involving 356 subjects was conducted to test this model. The results indicate that customer loyalty is influenced by perceived enjoyment, social norms and preference. Perceived cohesion has an indirect impact on loyalty. In addition, the finding’s practical implication suggests that community managers must overcome the problems users encounter, including suffering from an unstable system, malicious players and grief players.     

区块链技术在数字资产安全交易中的应用

针对传统数据资产交易平台依靠中心化的管理机构完成交易过程,不能保证数据资产交易过程中的安全性的弊端,利用区块链技术去中心化、去信任、难以篡改等技术特征.提出了一种基于区块链技术的新型数字资产安全交易方法.首先阐明传统数字资产交易平台的弊端,剖析了区块链技术在数据资产安全交易中的关键技术;其次,分别从数据存储、交易信息加密、验证节点间的共识算法方面提出具体的实施方案;最后,针对数据资产交易过程中的验证节点共识算法进行验证分析,实验结果表明,本文所提出的方法能很好的适用于数字资产安全交易.     

基于MS聚类分析模型的数据挖掘应用探讨

研究数据挖掘算法中的Microsoft聚类算法以及其在金融领域的应用。从海量的数据里挖掘出潜在的信息是数据挖掘的主要工作,通过对客户交易信息的过滤和挖掘,建立起为银行更好地提供智能决策和建议数据挖掘商业应用实例系统。系统的客户端开发选择的是Visual Studio.NET 2008,并使用ADOMD.NET对象及Web控件对模型的结果进行输出展示。用户可以应用这个系统通过输入客户的一些个人属性以及办理业务的基本情况,查看所关心的信誉情况、业务的办理趋向、银行开展新业务的趋向等信息。在整个实例系统的构建过程中,对聚类分析模型的挖掘过程进行了详细的分析,促进了数据挖掘的应用实践。     

实时主存数据库事务的预处理

本文提出了一种支持实时事务的预分析算法和基于该算法的主存数据库内外存替换策略．它首先预分析实时事务以获得事务存取行为的知识，再在事务执行时，进行基于这些信息的存取及内外存数据交换，从而实现主存数据库管理和支持实时事务的定时限制.     

Visual Exploration of Financial Data with Incremental Domain Knowledge

Modelling the dynamics of a growing financial environment is a complex task that requires domain knowledge, expertise and access to heterogeneous information types. Such information can stem from several sources at different scales, complicating the task of forming a holistic impression of the financial landscape, especially in terms of the economical relationships between firms. Bringing this scattered information into a common context is, therefore, an essential step in the process of obtaining meaningful insights about the state of an economy. In this paper, we present Sabrina 2.0, a Visual Analytics (VA) approach for exploring financial data across different scales, from individual firms up to nation-wide aggregate data. Our solution is coupled with a pipeline for the generation of firm-to-firm financial transaction networks, fusing information about individual firms with sector-to-sector transaction data and domain knowledge on macroscopic aspects of the economy. Each network can be created to have multiple instances to compare different scenarios. We collaborated with experts from finance and economy during the development of our VA solution, and evaluated our approach with seven domain experts across industry and academia through a qualitative insight-based evaluation. The analysis shows how Sabrina 2.0 enables the generation of insights, and how the incorporation of transaction models assists users in their exploration of a national economy.     

Improving Customer Retention in E-Commerce through a Secure and Privacy-Enhanced Loyalty System

Loyalty systems provide an interesting possibility for vendors in customer relationship management. This holds for both real world and online vendors. Many vendors apply loyalty systems to collect customer-specific data that may be exploited for many reasons, e.g., price discrimination and direct marketing. As a consequence, beside some potential benefits of a loyalty system, customers may also fear an invasion of privacy, and thus often refuse to participate in such programs. Thus, a vendor may have problems to turn privacy sensitive people into loyal customers using a typical loyalty system. In this paper, we present two variants of a privacy-friendly loyalty system to be used by online vendors for issuing loyalty points. The systems prevent vendors from exploiting data for the creation of customer profiles by providing unconditional unlinkability of loyalty points with regard to purchases. We propose a simple token-based approach and a counter-based approach which is much more efficient while preserving the privacy and security properties. Furthermore, the counter-based loyalty system prevents pooling of loyalty points which were issued to distinct customers. Matthias Enzmann received his diploma in computer science from the Technical University of Darmstadt, Germany, in 1999. In 1996, he started working with the TKT institute of GMD - German National Research Centre for Information Technology GmbH which in 2001 became Fraunhofer Institute for Secure Telecooperation due to the merger of GMD and Fraunhofer Gesellschaft. Since 1999 he holds the position of a regular researcher at Fraunhofer SIT. Currently, his research interests focus on privacy protection in electronic business processes, agent based mediation, and pseudonym systems. Markus Schneider received his diploma in electrical engineering with specialization on communications engineering. Afterwards, he started to work in the area of information and communication security and received his Doctor degree in electrical engineering. Currently, he is with the Fraunhofer Institute for Secure Telecooperation (SIT) in Darmstadt, Germany. His research interests include the development and application of security technologies in communications, and security and privacy issues in electronic business processes.     

区块链数据库:一种可查询且防篡改的数据库

随着比特币、以太币等一系列加密货币的兴起,其底层的区块链技术受到越来越广泛的关注.区块链有防篡改、去中心化的特性.以太坊利用区块链技术来构建新一代去中心化的应用平台.BigchainDB将区块链技术与传统的分布式数据库相结合,利用基于联盟投票的共识机制改进传统Pow机制中的节点全复制问题,提高了系统的扩展性与吞吐率.但是现有的区块链系统存储的信息大都是固定格式的交易信息,虽然在每个交易里有数据字段,但是现有的区块链系统并不能经由链上对交易内的数据字段的具体细节进行直接查询.如果想要查询数据字段的具体细节,只能先根据交易的哈希值进行查询,得到该交易的完整信息,然后再检索该交易内的数据信息.数据可操作性低,不具备传统数据库的查询功能.首先提出一种区块链数据库系统框架,将区块链技术应用于分布式数据管理;其次提出一种基于哈希指针的不可篡改索引,根据该索引快速检索区块内数据,以此实现区块链的查询;最后,通过实验测试数据库的读写性能,实验结果表明,所提出的不可篡改索引在保证不可篡改的同时具有较好的读写性能.     

Efficient Processing of Client Transactions in Real-Time

In traditional client-server databases, a transaction and its requisite data have to be colocated at a single site for the operation to proceed. This has usually been achieved by moving either the data or the transaction. However, the availability of high-bandwidth networking options has led users of today's systems to expect real-time guarantees about the completion time of their tasks. In order to offer such guarantees in a client-server database system, a transaction should be processed by any means that allows it to meet its deadline. To this end, we explore the option of moving both transactions and data to the most promising sites for successful completion. We propose a load-sharing framework that oversees the shipment of data and transactions so as to increase the efficiency of a cluster consisting of a server and a number of clients. Here, efficiency is defined as the percentage of transactions successfully completed within their deadlines by the cluster. The suitability of a client for processing a transaction is measured with respect to the availability of the transaction's required data in its local cache. In addition to the load-sharing algorithm, we use the concept of grouped locks, along with transaction deadline information, in order to schedule the movement of data objects in the cluster in a more efficient manner. We evaluate the real-time processing performance of the client-server architecture using detailed experimental testbeds. Our evaluation indicates that it is possible, in many situations, to achieve better performance than a centralized system.     

Coalitions in coordinated multi-agent production scheduling: A computational study

This paper studies distributed production scheduling where agents control dispersed information and decentralized decision authority. Using the classical job shop scheduling model, the effects of coalition formation and local communication in an iterative auction are studied. The case is investigated where job agents are allowed to form coalitions, where coalition members share private information and resolve resource conflicts among themselves, while intercoalition communication is limited to bidding. The computational study shows that when the size, type, and timing of the coalitions are properly determined, it is possible to produce a high-quality schedule with a reasonable number of iterations. The results show further improvement in convergence and in solution quality when coalition size and update frequency increase. However, these improvements show diminishing return; thus, it is concluded that a high-quality schedule can be achieved with manageable coalition sizes and a moderate level of information sharing.     

Direct mail promotion mechanisms and their application in supermarkets

In this study, we consider the problem of selecting supermarket loyalty program members to receive physical direct mail and promotional electronic direct mail (i.e., direct email). To help marketers choose the target members for physical direct mails, we modify the customer’s preference index of our original model to predict members’ repurchase rates for a physical supermarket’s members. Based on members’ predicted repurchase rates, marketers can design proper marketing strategies for different types of supermarket member to improve marketing effectiveness. In addition, because members can only spend a short amount of time reading direct emails before choosing the products that they like, a recommender system based on a simple combination method is introduced. The system determines the most suitable combination of commodity types under the condition that a customized direct email can include only a small, fixed number of such types. In this study, member transaction records from a well-known Taiwanese supermarket were used as the test data. This supermarket’s marketing department reviewed all the experimental results and confirmed that our approach is not only superior to the current approach employed by the supermarket but also useful in designing appropriate direct-mail marketing strategies for selected supermarket members. Our approach is also suitable for direct email sent by the supermarket.