10,938 research outputs found
Automatic vs Manual Provenance Abstractions: Mind the Gap
In recent years the need to simplify or to hide sensitive information in
provenance has given way to research on provenance abstraction. In the context
of scientific workflows, existing research provides techniques to semi
automatically create abstractions of a given workflow description, which is in
turn used as filters over the workflow's provenance traces. An alternative
approach that is commonly adopted by scientists is to build workflows with
abstractions embedded into the workflow's design, such as using sub-workflows.
This paper reports on the comparison of manual versus semi-automated approaches
in a context where result abstractions are used to filter report-worthy results
of computational scientific analyses. Specifically; we take a real-world
workflow containing user-created design abstractions and compare these with
abstractions created by ZOOM UserViews and Workflow Summaries systems. Our
comparison shows that semi-automatic and manual approaches largely overlap from
a process perspective, meanwhile, there is a dramatic mismatch in terms of data
artefacts retained in an abstracted account of derivation. We discuss reasons
and suggest future research directions.Comment: Preprint accepted to the 2016 workshop on the Theory and Applications
of Provenance, TAPP 201
What Automated Planning Can Do for Business Process Management
Business Process Management (BPM) is a central element of today organizations. Despite over the years its main focus has been the support of processes in highly controlled domains, nowadays many domains of interest to the BPM community are characterized by ever-changing requirements, unpredictable environments and increasing amounts of data that influence the execution of process instances. Under such dynamic conditions, BPM systems must increase their level of automation to provide the reactivity and flexibility necessary for process management. On the other hand, the Artificial Intelligence (AI) community has concentrated its efforts on investigating dynamic domains that involve active control of computational entities and physical devices (e.g., robots, software agents, etc.). In this context, Automated Planning, which is one of the oldest areas in AI, is conceived as a model-based approach to synthesize autonomous behaviours in automated way from a model. In this paper, we discuss how automated planning techniques can be leveraged to enable new levels of automation and support for business processing, and we show some concrete examples of their successful application to the different stages of the BPM life cycle
Visual analysis of sensor logs in smart spaces: Activities vs. situations
Models of human habits in smart spaces can be expressed by using a multitude of representations whose readability influences the possibility of being validated by human experts. Our research is focused on developing a visual analysis pipeline (service) that allows, starting from the sensor log of a smart space, to graphically visualize human habits. The basic assumption is to apply techniques borrowed from the area of business process automation and mining on a version of the sensor log preprocessed in order to translate raw sensor measurements into human actions. The proposed pipeline is employed to automatically extract models to be reused for ambient intelligence. In this paper, we present an user evaluation aimed at demonstrating the effectiveness of the approach, by comparing it wrt. a relevant state-of-the-art visual tool, namely SITUVIS
Web Data Extraction, Applications and Techniques: A Survey
Web Data Extraction is an important problem that has been studied by means of
different scientific tools and in a broad range of applications. Many
approaches to extracting data from the Web have been designed to solve specific
problems and operate in ad-hoc domains. Other approaches, instead, heavily
reuse techniques and algorithms developed in the field of Information
Extraction.
This survey aims at providing a structured and comprehensive overview of the
literature in the field of Web Data Extraction. We provided a simple
classification framework in which existing Web Data Extraction applications are
grouped into two main classes, namely applications at the Enterprise level and
at the Social Web level. At the Enterprise level, Web Data Extraction
techniques emerge as a key tool to perform data analysis in Business and
Competitive Intelligence systems as well as for business process
re-engineering. At the Social Web level, Web Data Extraction techniques allow
to gather a large amount of structured data continuously generated and
disseminated by Web 2.0, Social Media and Online Social Network users and this
offers unprecedented opportunities to analyze human behavior at a very large
scale. We discuss also the potential of cross-fertilization, i.e., on the
possibility of re-using Web Data Extraction techniques originally designed to
work in a given domain, in other domains.Comment: Knowledge-based System
The Pulse of Fileless Cryptojacking Attacks: Malicious PowerShell Scripts
Fileless malware predominantly relies on PowerShell scripts, leveraging the
native capabilities of Windows systems to execute stealthy attacks that leave
no traces on the victim's system. The effectiveness of the fileless method lies
in its ability to remain operational on victim endpoints through memory
execution, even if the attacks are detected, and the original malicious scripts
are removed. Threat actors have increasingly utilized this technique,
particularly since 2017, to conduct cryptojacking attacks. With the emergence
of new Remote Code Execution (RCE) vulnerabilities in ubiquitous libraries,
widespread cryptocurrency mining attacks have become prevalent, often employing
fileless techniques. This paper provides a comprehensive analysis of PowerShell
scripts of fileless cryptojacking, dissecting the common malicious patterns
based on the MITRE ATT&CK framework.Comment: 10 pages, 1 figur
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