Recursion Aware Modeling and Discovery For Hierarchical Software Event Log Analysis (Extended)

This extended paper presents 1) a novel hierarchy and recursion extension to the process tree model; and 2) the first, recursion aware process model discovery technique that leverages hierarchical information in event logs, typically available for software systems. This technique allows us to analyze the operational processes of software systems under real-life conditions at multiple levels of granularity. The work can be positioned in-between reverse engineering and process mining. An implementation of the proposed approach is available as a ProM plugin. Experimental results based on real-life (software) event logs demonstrate the feasibility and usefulness of the approach and show the huge potential to speed up discovery by exploiting the available hierarchy.Comment: Extended version (14 pages total) of the paper Recursion Aware Modeling and Discovery For Hierarchical Software Event Log Analysis. This Technical Report version includes the guarantee proofs for the proposed discovery algorithm

Discovering the network topology: an efficient approach for SDN

Network topology is a physical description of the overall resources in the network. Collecting this information using efficient mechanisms becomes a critical task for important network functions such as routing, network management, quality of service (QoS), among many others. Recent technologies like Software-Defined Networks (SDN) have emerged as promising approaches for managing the next generation networks. In order to ensure a proficient topology discovery service in SDN, we propose a simple agents-based mechanism. This mechanism improves the overall efficiency of the topology discovery process. In this paper, an algorithm for a novel Topology Discovery Protocol (SD-TDP) is described. This protocol will be implemented in each switch through a software agent. Thus, this approach will provide a distributed solution to solve the problem of network topology discovery in a more simple and efficient way.Peer ReviewedPostprint (published version

Discovering Restricted Regular Expressions with Interleaving

Discovering a concise schema from given XML documents is an important problem in XML applications. In this paper, we focus on the problem of learning an unordered schema from a given set of XML examples, which is actually a problem of learning a restricted regular expression with interleaving using positive example strings. Schemas with interleaving could present meaningful knowledge that cannot be disclosed by previous inference techniques. Moreover, inference of the minimal schema with interleaving is challenging. The problem of finding a minimal schema with interleaving is shown to be NP-hard. Therefore, we develop an approximation algorithm and a heuristic solution to tackle the problem using techniques different from known inference algorithms. We do experiments on real-world data sets to demonstrate the effectiveness of our approaches. Our heuristic algorithm is shown to produce results that are very close to optimal.Comment: 12 page

A Heuristic Approach for Discovering Reference Models by Mining Process Model Variants

Recently, a new generation of adaptive Process-Aware Information Systems (PAISs) has emerged, which enables structural process changes during runtime while preserving PAIS robustness and consistency. Such flexibility, in turn, leads to a large number of process variants derived from the same model, but differing in structure. Generally, such variants are expensive to configure and maintain. This paper provides a heuristic search algorithm which fosters learning from past process changes by mining process variants. The algorithm discovers a reference model based on which the need for future process configuration and adaptation can be reduced. It additionally provides the flexibility to control the process evolution procedure, i.e., we can control to what degree the discovered reference model differs from the original one. As benefit, we can not only control the effort for updating the reference model, but also gain the flexibility to perform only the most important adaptations of the current reference model. Our mining algorithm is implemented and evaluated by a simulation using more than 7000 process models. Simulation results indicate strong performance and scalability of our algorithm even when facing large-sized process models

Massive NGS data analysis reveals hundreds of potential novel gene fusions in human cell lines

Background: Gene fusions derive from chromosomal rearrangements and the resulting chimeric transcripts are often endowed with oncogenic potential. Furthermore, they serve as diagnostic tools for the clinical classification of cancer subgroups with different prognosis and, in some cases, they can provide specific drug targets. So far, many efforts have been carried out to study gene fusion events occurring in tumor samples. In recent years, the availability of a comprehensive Next Generation Sequencing dataset for all the existing human tumor cell lines has provided the opportunity to further investigate these data in order to identify novel and still uncharacterized gene fusion events. Results: In our work, we have extensively reanalyzed 935 paired-end RNA-seq experiments downloaded from "The Cancer Cell Line Encyclopedia" repository, aiming at addressing novel putative cell-line specific gene fusion events in human malignancies. The bioinformatics analysis has been performed by the execution of four different gene fusion detection algorithms. The results have been further prioritized by running a bayesian classifier which makes an in silico validation. The collection of fusion events supported by all of the predictive softwares results in a robust set of ∼ 1,700 in-silico predicted novel candidates suitable for downstream analyses. Given the huge amount of data and information produced, computational results have been systematized in a database named LiGeA. The database can be browsed through a dynamical and interactive web portal, further integrated with validated data from other well known repositories. Taking advantage of the intuitive query forms, the users can easily access, navigate, filter and select the putative gene fusions for further validations and studies. They can also find suitable experimental models for a given fusion of interest. Conclusions: We believe that the LiGeA resource can represent not only the first compendium of both known and putative novel gene fusion events in the catalog of all of the human malignant cell lines, but it can also become a handy starting point for wet-lab biologists who wish to investigate novel cancer biomarkers and specific drug targets

Detecting Missing Dependencies and Notifiers in Puppet Programs

Puppet is a popular computer system configuration management tool. It provides abstractions that enable administrators to setup their computer systems declaratively. Its use suffers from two potential pitfalls. First, if ordering constraints are not specified whenever an abstraction depends on another, the non-deterministic application of abstractions can lead to race conditions. Second, if a service is not tied to its resources through notification constructs, the system may operate in a stale state whenever a resource gets modified. Such faults can degrade a computing infrastructure's availability and functionality. We have developed an approach that identifies these issues through the analysis of a Puppet program and its system call trace. Specifically, we present a formal model for traces, which allows us to capture the interactions of Puppet abstractions with the file system. By analyzing these interactions we identify (1) abstractions that are related to each other (e.g., operate on the same file), and (2) abstractions that should act as notifiers so that changes are correctly propagated. We then check the relationships from the trace's analysis against the program's dependency graph: a representation containing all the ordering constraints and notifications declared in the program. If a mismatch is detected, our system reports a potential fault. We have evaluated our method on a large set of Puppet modules, and discovered 57 previously unknown issues in 30 of them. Benchmarking further shows that our approach can analyze in minutes real-world configurations with a magnitude measured in thousands of lines and millions of system calls