The lateral movement phase in Advanced Persistent Threats (APTs) represents a critical stage where attackers navigate internal systems to expand their foothold and escalate privileges. To improve the detection of these threats, this paper presents a process mining-based approach for detecting APTs, focusing specifically on the lateral movement phase. A controlled experimental environment was developed to simulate attack scenarios, enabling the collection and analysis of system and process logs. The results demonstrate the feasibility of using process mining to model attack progression, enhance incident response, and support the early detection of stealthy intrusions in Windows-based environments.

Background: The generation of synthetic event logs is fundamental to the advancement and evaluation of process mining techniques. However, synthetic log generation in the context of multi-perspective declarative modeling faces challenges due to its flexible nature, where any behavior that is not constrained by rules is allowed. In addition, the synthetic event log needs to simultaneously meet process, data access and privacy constraints. Objective: This study presents a general-purpose, configurable, multi-perspective log generator capable of simultaneously ensuring compliance with the rules of the flow, data access and privacy perspectives in a flexible context such as that found in declarative processes. Method: A multi-perspective synthetic log generator was developed, capable of integrating declarative process models, data access and organizational structure. The tool was structured in three stages: processing the input models, generating the event log with Declare4Py, and generating the access log respecting the restrictions. Results: Two experiments were carried out with 1,000 and 2,000 cases respectively. The logs generated respected all the rules of conformity between activities, accesses and permissions. There was a linear increase in file size and a moderate increase in execution time, compatible with the complexity of the models. Conclusions: The proposed tool proved effective in generating coherent synthetic logs with multiple perspectives, supporting research into process mining and the evaluation of compliance algorithms. It is a relevant solution for contexts where real data is scarce or sensitive.

Context: Process Mining (PM) aims to discover processes and their characteristics from event logs recorded by information systems. There are dozens of general-purpose tools. The Brazilian judiciary wants to make the technology available to magistrates with little or no knowledge of the field of PM. Problem: The usability and comprehensibility of the available tools prevent their adoption by laypeople. In fact, these are two of the eleven challenges proposed by the IEEE Task Force on PM that are still little explored in the context of non-specialists. Methodology: Applied qualitative research, using User Centered Design (UCD) principles to guide the construction of the JuMP tool, conceived considering anthropological and sociological aspects of the Brazilian judiciary. For a year and six months, a team of PM specialists worked with representatives of the judiciary sector to produce, evaluate and evolve the new product. An experiment was performed to evaluate the usability and understandability of JuMP. Results: The study demonstrated that the use of a PM tool oriented to the application domain is fundamental for domain experts with little or no knowledge in PM to be able to make good use of it. Contribution: Demonstration that, unlike the prevailing practice in the area of PM, which prioritizes the provision of general purpose tools, the design of tools oriented to the application domain are prerequisites to improve usability and comprehensibility attributes. Finally, the work proposes a generic and adaptable methodology for developing specific-purpose PM tools and the experience of its implications to the design of the JuMP tool.

Context: The blended learning (or b-learning), has grown over the years. However, few studies have been carried out on the effectiveness of entrepreneurial training in the blended modality. Objective: The general objective of the study is to evaluate the effectiveness of the learning experience in the blended modality experienced by adults for the development of entrepreneurial skills. Method: This work conducted a Digital Ethnography study by combining qualitative research techniques to capture the evidence of the participants' perceptions in the blended modality. The survey was conducted with adult entrepreneurs. Results: At the end of the study, it was possible to identify aspects of improvements in the usability of the platform, reflections on the teaching process, identification of points that reinforce the positive experience for the participants and a reduction in dropout from 40% of face-to-face education to 6.67% (1st class) and 4.35% (2nd class), and 0% (3rd class) in blended education. Conclusions: If proper engagement mechanisms are used, blended learning is an effective and efficient educational instrument for adult entrepreneurs, which usually have busy daily routines and need flexibility in their study schedule.

Systems unavailability may produce severe consequences for modern business such as data loss, customer dissatisfaction, and subsequent revenue loss. Disaster recovery (DR) solutions have been adopted by many organizations as an attempt to prevent data loss and ensure business continuity. With the cloud computing expansion, different cloud providers have been offering low-cost solutions for DR purposes such as the Backup-as-a-service (BaaS) for consumers. Therefore, in this paper, we present an integrated model-experiment approach to evaluate a BaaS environment for DR purposes. We use analytic models and fault-injection experiments to evaluate DR keymetrics such as availability, downtime, Recovery Time Objective (RTO), and Recovery Point Objective (RPO) in a real-world BaaS environment. The results revealed that the environment availability can vary according to the amount of data to backed up and restored. Besides, a sensitivity analysis shows that the RTO and RPO are mainly influenced by the the mean time to recover from a disaster and the backup interval, respectively.

Relational databases are the most popular database system worldwide. The occurrence of failures in these systems may produce severe consequences for the business, such as data loss, customer dissatisfaction, and subsequent revenue loss. Consequently, many organizations have adopted disaster recovery (DR) solutions as an attempt to prevent data loss and ensure business continuity. Data replication for databases is one of the most used DR solution employed to guarantee data safety and availability. However, the analysis regarding DR aspects has been less explored. Therefore, in this paper, we present an integrated model-experiment approach to evaluate replication mechanisms in relational databases for DR purposes. We performed experiments in a geo-distributed cloud environment and developed analytic models to evaluate DR key-metrics such as availability, downtime, Recovery Time Objective (RTO), and Recovery Point Objective (RPO). The results revealed that the adoption of replication mechanisms could increase the system's availability significantly. It also revealed that the replication mechanisms can guarantee RPO and RTO within seconds.

The Information Technology (IT) systems of most organizations must support their operations 24 hours a day, 7 days a week. Systems unavailability may have serious consequences such as data loss, customer dissatisfaction, and subsequent revenue loss. With the popularity of cloud computing, the adoption of cloud-based disaster recovery (DR) solutions has gained more space to prevent data loss and ensure business continuity. However, disaster recovery solutions are not cheap and do not exist as a single solution that suits every requirement (e.g., availability and costs). In this paper, we present an integrated model-experiment approach to evaluate cloud-based disaster recovery solutions. We use Stochastic Petri Nets (SPNs) and fault-injection experiments to evaluate availability related metrics like steady-state availability and downtime. To demonstrate the feasibility of our approach, distinct real-world cloud-based DR solutions (e.g., active/active and active/standby) are modeled and analyzed. The results revealed that disaster recovery solution significantly improves system availability and minimizes the downtime costs. In addition, our numerical analysis shows the statistical correspondence between the results of the experiments and models.

Threats to information security can have great impact on business finances and company's reputation. Traditional methodologies for evaluating the maturity of data centers investigate security parameters to determine the compliance of data centers and international security norms. This paper proposes two innovative evaluation procedures to capture other security perspectives on data center environments: (1) weighted analysis - it weights higher security controls simultaneously present in a higher number of norms; (2) it is sensitive to the importance level that the organization assigns to each security control. Through the proposed methodology, security engineers can identify security issues, characterize the security maturity, and suggest new policies improve security configurations of data centers. This paper also includes a case study to evaluate the benefits of the methodology in real-world scenarios. Results demonstrated that the proposed methodology evaluates higher the security elements more relevant for the company, where as traditional approaches consider all security aspects to be equally important.

Service-Oriented Architecture (SOA) is a paradigm for software development that has been increasingly adopted for factory automation. In SOA, services are independently developed and a central engine orchestrates their functional behavior according to the process workflow. If on one hand this orchestration is required to maximize performance and productivity, i.e., the software is required to be maximally permissive, on the other hand, implementing a service orchestrator is a creative task which cannot be totally automated. Furthermore, industrial processes tend to be very large, making it difficult to empirically provide in-advance quality guarantees for industrial SOA-based applications. In this paper, we show how maximally permissive and deadlock-free service orchestrators can be implemented. We propose a model for each activity that compose a SOA programming language. Then, we show how pieces of a workflow can be individually represented by combining activity models. Afterwards, we specify the logical behavior of the workflow in order to organize those pieces and reproduce the orchestration effect. By using controllability concepts, we finally compute from the orchestrator a version of it that formally provides certain quality guarantees. Examples illustrate the approach.

The demand for mobile devices and applications through the marketplaces has increased dramatically in recent years. Mobile applications take on even greater prominence and importance in the IT industry. Along with the growth of mobile technology, battery lifetime has become one of the most relevant challenges for this kind of device in the last few years. This paper presents a study assessing performance and energy consumption for mobile applications by means of stochastic models. In order to assist inexperienced system designers to create formal models, mapping rules were adopted to transform SysML diagrams into Deterministic and Stochastic Petri nets. The results of this work can help system designers in the design-making process in order to develop more efficient mobile applications. The estimates obtained from the models show that the proposed approach is indeed a good approximation to the respective measures obtained from the real infrastructure.

Declarative Business Processes offer more flexibility to business processes by the use of business rules. Such business rules describe what must or must not be done during the process execution, but do not prescribe how. To fully experience the benefits of this modeling approach, companies need a rule engine capable of checking the rules and guiding the user through the execution of the process. The rule engines available today present several limitations that impair their use to this application. In particular, the well-known approach that employs Linear Temporal Logic (LTL) has the drawback of the state space explosion as the process model grows. This paper proposes a novel graph-based rule engine that does not share the problems presented by other engines, being better suited to model declarative business processes than the techniques currently in use.

Although it is assumed that the implementation of design by contract is better modularized by means of aspect-oriented (AO) programming, there is no empirical evidence on the effectiveness of AO for modularizing non-trivial design by contract code in realistic development scenarios. This paper reports a quantitative and qualitative case study that evolves a real-life application to assess various facets of the adequacy of aspects for modularizing the design by contract concern. Our evaluation focused upon a number of system changes that are typically performed during software maintenance tasks. The study was driven by an analysis of fundamental modularity attributes, such as separation of concerns, coupling, conciseness, and change propagation. We have found that AO techniques improved separation of concerns and the design stability between the design by contract code and base application code throughout the development scenarios. However, contradicting the general intuition, the AO versions of the system did not present significant gains regarding four classical size metrics we employed.

Customer requirements and local legislation are forcing companies to provide green products and services. Such driving forces have resulted in the study of green supply chains (SCs), which are SCs that take into account factors that may cause positive or negative environmental impacts. However, some environmental indicators are often at odds with business goals, leading to a conflict, which, in order to optimize several goals simultaneously, is usually resolved by utilizing multicriteria decision analysis. Other modeling techniques describe systems by considering temporal aspects and causal relations. Such is the case of Petri nets, which were previously widely used to analyze performance in the context of SCs. This paper presents a method that combines the strengths of both techniques to assess the environmental and business indicators of such systems. This method adopts the exergy and the global warming potential key environmental indicators. A case study is conducted to demonstrate the application of the proposed modeling technique.

The subject of business-IT alignment has increasingly attracted the attention of executives in the past decade. The purpose is to leverage the potential of IT to improve business performance. This goal can be achieved by developing mechanisms to enhance the communication between business and IT. In the same track, its essential to apply methods to assure that IT investments are driven by the strategic goals and priorities of the company. Service-Oriented Architecture (SOA)is widely recognized as a technology that promotes IT alignment and business agility. However, strategic goals of firms have been frequently neglected when dealing with technical subjects such as web service selection and deployment. In this work, we propose a multiple criteria decision method based on the Analytic Hierarchy Process (AHP) that is able to accommodate in a single framework both the technical and the strategic requirements of web service deployment and selection. Our framework help IT managers to find the configuration that best translates the preferences of the customer and the priorities of the company. A numeric case study compares our approach to other traditional uses of AHP.

To address the need for evaluation techniques for complex business processes, also known as workflows, this paper proposes an approach based on generalized stochastic Petri nets (GSPNs). We review ten related approaches published in the last fifteen years and compare them to our approach using a wide range of criteria. On the basis of this evaluation, we observe that the newly proposed approach provides results that are at least as good as those from the most accepted alternatives and holds a number of additional advantages, such as modeling simplicity, improved precision, and model reuse for qualitative analyses. The overall approach is formally defined in this paper, along with the definition of several performance metrics. Part of these metrics can be computed analytically, while the remainder can be obtained by simulating the GSPN. Furthermore, a tool has been developed to translate automatically business process execution language processes into GSPNs. Finally, we present a case study in which we applied the proposed approach, colored Petri net tools, and an industrial tool to obtain performance insights into a realistic workflow. The results were highly similar, demonstrating the feasibility and the accuracy of our approach.

Design by Contract (DbC) is a technique for developing and improving functional software correctness through definition of "contracts" between client classes and their suppliers. Such contracts are enforced during runtime and if any of them is violated a runtime error should occur. Runtime assertions checkers (RACs) are a well-known technique that enforces such contracts. Although they are largely used to implement the DbC technique in contemporary languages, like Java, studies have shown that characteristics of contemporary exception handling mechanisms can discard contract violations detected by RACs. As a result, a contract violation may not be reflected in a runtime error, breaking the supporting hypothesis of DbC. This paper presents an error recovery technique for RACs that tackles such limitations. This technique relies on aspect-oriented programming in order to extend the functionalities of existing RACs stopping contract violations from being discarded. We applied the recovery technique on top of five Java-based contemporary RACs (i.e., JML/jml, JML/ajml, JContractor, CEAP, and Jose). Preliminary results have shown that the proposed technique could actually prevent the contract violations from being discarded regardless of the characteristics of the exception handling code of the target application.

Scheduling is an important tool for optimizing the performance of parallel systems. It aims at reducing the completion time of parallel applications by properly allocating the tasks to the processors. This work proposes a novel scheduling algorithm to parallelize tasks with dependence restrictions. The communication costs between processors and computer architecture are parameters of the proposed algorithm, which explores the trade off between process execution time and communication costs between processes to optimize the system's overall performance. The paper conducts an experiment to compare the performance of the proposed algorithm against six other scheduling algorithms. The experiment considered several execution scenarios. Although our algorithm does not present the best performance in any of the execution scenarios, it produces the best average execution time for the scenarios studied.

In this paper we describe the application of process mining techniques to analyze a software development process. Software engineering practitioners often conduct quality auditing of the development process to assure conformance with organizational standards. Despite some works have explored process mining techniques for the conformance analysis of general business processes, it is not of our knowledge any study that applies process mining to conformance checking of software development processes. Under a practical perspective, this paper explores a real database with event logs generated in the past five years of execution of a software development process. The database was gently provided by a Brazilian software house with annual revenue of more than US$ 500 million and includes more than 2,000 cases (process instances). The results show that process mining can be effectively employed as a supporting tool for the management of software development processes and for the improvement of the maturity level of software engineering organizations.

Real problem simulations involving physic phenomena can demand too much execution time. To improve the performance of these simulations it is necessary to have an approach to parallelize the processes that compose the simulation. MPhyScaS (Multi-Physics and Multi-Scale Solver Environment) is an environment dedicated to the automatic development of simulators. Each MPhyScaS simulation demands a great amount of time. To parallelize MPhyScaS simulations, the approach used should define a hierarchical parallel structure. The aim of the work herein presented is to improve the performance of clusters in the processing of MPhyScaS simulations which are composed by a set of dependent tasks by scheduling them. The presented model is based on Genetic Algorithms (GA) to schedule the parallel tasks following MPhyScaS architecture dependence restrictions. The communication between processors must also be considered in this scheduling. Therefore, a trade off must be found between the execution of processes and the time necessary for these processes to communicate with each other.

Real problem simulations involving physic phenomena can demand too much execution time. To improve the performance of these simulations it is necessary to have an approach to parallelize the processes that compose the simulation. MPhyScaS (Multi-Physics and Multi-Scale Solver Environment) is an environment dedicated to the automatic development of simulators. Each MPhyScaS simulation demands a great amount of time. To parallelize MPhyScaS simulations, the approach used should define a hierarchical parallel structure. The aim of the work herein presented is to identify parallel jobs and dependent ones. The presented model is based on Coloured Petri Nets (CPN). This information will be input to a scheduling algorithm.

The Service-Oriented Architecture (SOA) has become a unifying technical architecture that may be embodied through Web Service technologies. Predicting the variable behavior of SOA systems can mean a way to improve the quality of the business transactions. This paper proposes a simulation modeling approach based on stochastic Petri nets to estimate the performance of SOA-applications. Using the proposed model is possible to predict resource consumption and service levels degradation in scenarios with different compositions and workloads, even before developing the application. A case study was conducted in order to validate our approach, comparing its accuracy with the results from an analytical model existent in the literature.

This paper proposes a methodology for modeling and evaluating supply chains based ongeneralized stochastic Petri net(GSPN) components. The proposed modeling process is based on a bottom-up approach, which assures desirable model properties that start from a set of predefined modules for typical supply chain entities. A compositional algebra that formally defines GSPN compositions is presented. The proposed methodology is supported by an implementation of the methodology in thestochastic logistics optimizer tool(SLOT). An industrial case study has been conducted to show the benefits of the approach.

In this paper we review several approaches for the performance evaluation of business workflows and propose an approach based on Generalized Stochastic Petri Nets, which presents desirable characteristics, such as analytic solutions. We present a case study demonstrating the application of our proposal and compare the results with that obtained by a colored Petri net model.

The service-oriented architecture (SOA) has become an unifying technical architecture that can be embodied with Web service technologies, in which the Web service is thought as a fundamental building block. This paper proposes a simulation modeling approach based on stochastic Petri nets to estimate the performance of SOA applications. Using the proposed model it is possible to predict resource consumption and service levels degradation in scenarios with different compositions and workloads. A case study was conducted to validate the approach and to compare the results against an existing analytical modeling approach.

The Java Modeling Language (JML) is a behavioral interface specification language (BISL) designed for Java. It was developed with the aim of improving the functional software correctness of Java applications. The JML compiler(jmlc) generates bytecodes that use the Java reflection mechanism and data structures not supported by Java ME applications. In order to overcome this limitation, we proposethe use of AspectJ to implement a new JML compiler, which generates an instrumented byte code compliant with both Java SE and Java ME applications. The paper also includes a comparative study to demonstrate the quality of the final code generated by our compiler. Results indicate that the overhead in code size produced by our compiler is very small, which is essential for Java ME applications.

This work proposes the use of pre-defined generalized stochastic Petri net (GSPN) components to model and evaluate outbound logistics. The modeling process takes into account the automatic generation of these stochastic models from high-level end-user-oriented descriptions. The model resultant from this translation is guaranteed to have some expected Petri nets' properties. The Stochastic Logistics Optimizer Tool (SLOT) was implemented with the aim of supporting the modeling process based on a component library. Case studies were carried out and one of them is presented.

Concurrent programs are essential in the development of web based information systems. The wide dissemination of these systems increased the need for methods to create correct and efficient concurrent programs, which are usually difficult to implement and test. This paper presents guidelines to improve the concurrent control structure of object-oriented software using aspect-oriented programming through AspectJ based on an existent object-oriented concurrency control implementation method. We have defined a simple aspect framework that can be extended to implement concurrency control in applications complying with a software architecture presented in this paper. The framework comprises a set of reusable aspects useful for modularizing concurrency control.

Cache memory hierarchy contributes positively to system performance. Moreover, tuning cache architectures in platforms for embedded applications can dramatically reduce energy consumption. This paper presents an automated method for adjusting two-level cache memory hierarchy intended for data caches in order to reduce energy consumption and improve the performance of embedded applications. We propose an automated mechanism called TEMGA (Two-level cache Exploration Mechanism based on Genetic Algorithm), to determine the suitable cache hierarchy configuration by exploring a small part of search space. In our experiments, we applied the proposed mechanism to 12 different benchmarks from the MiBench suite. The results show an average reduction of about 15% in the energy consumption for data caches when compared to existing heuristics and a reduction of 5 times in the number of cycles needed to execute applications from Mibench Benchmark suite.

The outbound logistics can determine the success or failure of an industry. It has a high participation in overall logistics costs and is a decisive factor for the Quality of Service (QoS). Measuring this activity and evaluating possible changes will help its management and can lead to a cost reduction and a better QoS. This paper proposes the use of predefined Generalized Stochastic Petri Nets (GSPN) components that allows the evaluation of vehicles utilization, storage levels and QoS indices. The model representing an outbound logistics scenario is obtained with a bottom-up approach, through the composition of these components, guaranteeing some expected GSPN properties. At the end of this paper, a study case made in a Brazilian meat processing industry is presented.

The globalized and highly competitive manufacturing industries have been imposing several challenges upon the field of production engineering. Tuning systems to high performance within cost constraints has been a reality and a critical factor of success for them. Hence, traditional performance metrics are not enough to supply these manufacturing industries. In order to improve the performance evaluation of such systems, this paper proposes a GSPN with energy consumption and human resources allocation annotations. The validation of the proposed model has been carried out through the execution of a case study inside a Brazilian meat processing industry.

The context of this work is related to embedded hard real-time systems development, more specifically, in the software generation phase. Embedded software has become much harder to design caused by the diversity of requirements and high complexity. Correctness and timeliness verification is an issue to be concerned. Usually, complex embedded real-time systems rely on specialized operating system kernels. However, operating systems may introduce significant overheads in execution time as well as in memory requirement. Software synthesis might be an alternative approach to operating systems usage, since it can generate tailored code for satisfying functional, performance, and resource constraints, and automatically generate runtime support (scheduling, resource management, communication, etc.) customized for each particular specification. However, the dispatcher and timer interrupt handler overheads are often neglect in software synthesis research. This paper provides a formal approach for system's modeling, and such model is adopted for synthesizing a timely and predictable scheduled code taking into account dispatcher and interrupt handler overheads.

Embedded hard real-time systems have stringent timing constraints that must be satisfied for the correct functioning of the system. Hence all tasks must be finished before their deadlines. In addition, there are systems where energy is another constraint that must also be satisfied. In this paper, a pre-runtime scheduling algorithm is presented in order to find schedules satisfying both timing and energy constraints. The proposed approach uses state space exploration for finding pre-runtime schedules. However, the main problem with such methods is the space size, which can exponentially grow. This paper tackles this problem through a depth-first search method for generating a partial timed labeled transition system derived from the time Petri net model.