A linear temporal logic model checking method over finite words with correlated transition attributes

Temporal logic model checking techniques are applied, in a natural way, to the analysis of the set of finite traces composing a system log. The specific nature of such traces helps in adapting traditional techniques in order to extend their analysis capabilities. The paper presents an adaption of the classical Timed Propositional Temporal Logic to the case of finite words and considers relations among different attributes corresponding to different events. The introduced approach allows the use of general relations between event attributes by means of freeze quantifiers as well as future and past temporal operators. The paper also presents a decision procedure, as well as a study of its computational complexity

Reducing the price of resource provisioning using EC2 spot instances with prediction models

The increasing demand of computing resources has boosted the use of cloud computing providers. This has raised a new dimension in which the connections between resource usage and costs have to be considered from an organizational perspective. As a part of its EC2 service, Amazon introduced spot instances (SI) as a cheap public infrastructure, but at the price of not ensuring reliability of the service. On the Amazon SI model, hired instances can be abruptly terminated by the service provider when necessary. The interface for managing SI is based on a bidding strategy that depends on non-public Amazon pricing strategies, which makes complicated for users to apply any scheduling or resource provisioning strategy based on such (cheaper) resources. Although it is believed that the use of the EC2 SIs infrastructure can reduce costs for final users, a deep review of literature concludes that their characteristics and possibilities have not yet been deeply explored. In this work we present a framework for the analysis of the EC2 SIs infrastructure that uses the price history of such resources in order to classify the SI availability zones and then generate price prediction models adapted to each class. The proposed models are validated through a formal experimentation process. As a result, these models are applied to generate resource provisioning plans that get the optimal price when using the SI infrastructure in a real scenario. Finally, the recent changes that Amazon has introduced in the SI model and how this work can adapt to these changes is discussed

Log-Based Session Profiling and Online Behavioral Prediction in E-Commerce Websites

Improvements to customer experience give companies a competitive advantage, as understanding customers' behaviors allows e-commerce companies to enhance their marketing strategies by means of recommendation techniques and the customization of products and services. This is not a simple task, and it becomes more difficult when working with anonymous sessions since no historical information of the user can be applied. In this article, analysis and clustering of the clickstreams of past anonymous sessions are used to synthesize a prediction model based on a neural network. The model allows for prediction of a user's profile after a few clicks of an online anonymous session. This information can be used by the e-commerce's decision system to generate online recommendations and better adapt the offered services to the customer's profile

Analysis of Users' Behavior in Structured e-Commerce Websites

Online shopping is becoming more and more common in our daily lives. Understanding users'' interests and behavior is essential to adapt e-commerce websites to customers'' requirements. The information about users'' behavior is stored in the Web server logs. The analysis of such information has focused on applying data mining techniques, where a rather static characterization is used to model users'' behavior, and the sequence of the actions performed by them is not usually considered. Therefore, incorporating a view of the process followed by users during a session can be of great interest to identify more complex behavioral patterns. To address this issue, this paper proposes a linear-temporal logic model checking approach for the analysis of structured e-commerce Web logs. By defining a common way of mapping log records according to the e-commerce structure, Web logs can be easily converted into event logs where the behavior of users is captured. Then, different predefined queries can be performed to identify different behavioral patterns that consider the different actions performed by a user during a session. Finally, the usefulness of the proposed approach has been studied by applying it to a real case study of a Spanish e-commerce website. The results have identified interesting findings that have made possible to propose some improvements in the website design with the aim of increasing its efficiency

Parallel computation of the reachability graph of petri net models with semantic information

Formal verification plays a crucial role when dealing with correctness of systems. In a previous work, the authors proposed a class of models, the Unary Resource Description Framework Petri Nets (U-RDF-PN), which integrated Petri nets and (RDF-based) semantic information. The work also proposed a model checking approach for the analysis of system behavioural properties that made use of the net reachability graph. Computing such a graph, specially when dealing with high-level structures as RDF graphs, is a very expensive task that must be considered. This paper describes the development of a parallel solution for the computation of the reachability graph of U-RDF-PN models. Besides that, the paper presents some experimental results when the tool was deployed in cluster and cloud frameworks. The results not only show the improvement in the total time required for computing the graph, but also the high scalability of the solution, which make it very useful thanks to the current (and future) availability of cloud infrastructures

Comment on "On the TST_S-Anomaly in Betaine Calcium Chloride Dihydrate"

Recently, Hlinka and Ishibashi [J. Phys. Soc. Jpn. 67, 495 (1998)] discussed the $T_S$-anomaly in betaine calcium chloride dihydrate (BCCD) in a Landau-type approach. We comment on the shortcomings of this approach and discuss the $T_S$-anomaly in the framework of a microscopical pseudo spin model based on a realistic description of BCCD in terms of symmetry-adapted local modes.Comment: 2 pages, RevTex, submitted to J. Phys. Soc. Jp

Геофизические особенности Верхнеюрского разреза месторождений углеводородов Томской области

Использованы петрофизические уравнения и данные геофизических исследований скважин месторождений углеводородов Томской области. Выявлены петрофизические типы коллекторов горизонта Ю1 и геофизические особенности пород баженовской свиты в разрезах с разным типом коллекторов

Handling Big(ger) logs: Connecting ProM 6 to apache hadoop

Within process mining the main goal is to support the analysis, im- provement and apprehension of business processes. Numerous process mining techniques have been developed with that purpose. The majority of these tech- niques use conventional computation models and do not apply novel scalable and distributed techniques. In this paper we present an integrative framework connect- ing the process mining framework ProM with the distributed computing environ- ment Apache Hadoop. The integration allows for the execution of MapReduce jobs on any Apache Hadoop cluster enabling practitioners and researchers to ex- plore and develop scalable and distributed process mining approaches. Thus, the new approach enables the application of different process mining techniques to events logs of several hundreds of gigabytes

RF-Based Location Using Interpolation Functions to Reduce Fingerprint Mapping

Indoor RF-based localization using fingerprint mapping requires an initial training step, which represents a time consuming process. This location methodology needs a database conformed with RSSI (Radio Signal Strength Indicator) measures from the communication transceivers taken at specific locations within the localization area. But, the real world localization environment is dynamic and it is necessary to rebuild the fingerprint database when some environmental changes are made. This paper explores the use of different interpolation functions to complete the fingerprint mapping needed to achieve the sought accuracy, thereby reducing the effort in the training step. Also, different distributions of test maps and reference points have been evaluated, showing the validity of this proposal and necessary trade-offs. Results reported show that the same or similar localization accuracy can be achieved even when only 50% of the initial fingerprint reference points are taken