AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges

Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes, particularly in cloud infrastructures, to provide actionable insights with the primary goal of maximizing availability. There are a wide variety of problems to address, and multiple use-cases, where AI capabilities can be leveraged to enhance operational efficiency. Here we provide a review of the AIOps vision, trends challenges and opportunities, specifically focusing on the underlying AI techniques. We discuss in depth the key types of data emitted by IT Operations activities, the scale and challenges in analyzing them, and where they can be helpful. We categorize the key AIOps tasks as - incident detection, failure prediction, root cause analysis and automated actions. We discuss the problem formulation for each task, and then present a taxonomy of techniques to solve these problems. We also identify relatively under explored topics, especially those that could significantly benefit from advances in AI literature. We also provide insights into the trends in this field, and what are the key investment opportunities

A Comparative Analysis of Process Instance Cluster Techniques

The application of process mining and analysis techniques to the process logs of information systems often leads to highly complex results, e.g. in terms of a high number of elements in the mined model. Thus, clustering corresponding log files is mandatory in the context of an expedient analysis. Against that background, many cluster techniques were developed during the last years but, at the same time, it is unclear how powerful they operate in particular application scenarios. Therefore, the paper at hand aims at analyzing and comparing the capabilities of existing cluster techniques with regard to different objectives. As a result, some techniques are more suitable for the handling of particular scenarios than others and there are also general challenges in their application, which should be addressed in future work

VaryMinions:Leveraging RNNs to Identify Variants in Event Logs

International audienc

Prototype Selection using Clustering and Conformance Metrics for Process Discovery

International audienceAutomated process discovery algorithms aim to automatically create process models based on event data that is captured during the execution of business processes. These algorithms usually tend to use all of the event data to discover a process model. Using all (i.e., less common) behavior may lead to discover imprecise and/or complex process models that may conceal important information of processes. In this paper, we introduce a new incremental prototype selection algorithm based on the clustering of process instances to address this problem. The method iteratively computes a unique process model from a different set of selected prototypes that are representative of whole event data and stops when conformance metrics decrease. This method has been implemented using both ProM and RapidProM. We applied the proposed method on several real event datasets with state-of-the-art process discovery algorithms. Results show that using the proposed method leads to improve the general quality of discovered process models

A Literature Review on Predictive Monitoring of Business Processes

Oleme läbi vaadanud mitmesuguseid ennetava jälgimise meetodeid äriprotsessides. Prognoositavate seirete eesmärk on aidata ettevõtetel oma eesmärke saavutada, aidata neil valida õige ärimudel, prognoosida tulemusi ja aega ning muuta äriprotsessid riskantsemaks. Antud väitekirjaga oleme hoolikalt kogunud ja üksikasjalikult läbi vaadanud selle väitekirja teemal oleva kirjanduse. Kirjandusuuringu tulemustest ja tähelepanekutest lähtuvalt oleme hoolikalt kavandanud ennetava jälgimisraamistiku. Raamistik on juhendiks ettevõtetele ja teadlastele, teadustöötajatele, kes uurivad selles valdkonnas ja ettevõtetele, kes soovivad neid tehnikaid oma valdkonnas rakendada.The goal of predictive monitoring is to help the business achieve their goals, help them take the right business path, predict outcomes, estimate delivery time, and make business processes risk aware. In this thesis, we have carefully collected and reviewed in detail all literature which falls in this process mining category. The objective of the thesis is to design a Predictive Monitoring Framework and classify the different predictive monitoring techniques. The framework acts as a guide for researchers and businesses. Researchers who are investigating in this field and businesses who want to apply these techniques in their respective field