Automatic crash bucketing is a crucial phase in the software development process for efficiently triaging bug reports. It generally consists in grouping similar reports through clustering techniques. However, with real-time streaming bug collection, systems are needed to quickly answer the question: What are the most similar bugs to a new one?, that is, efficiently find near-duplicates. It is thus natural to consider nearest neighbors search to tackle this problem and especially the well-known locality-sensitive hashing (LSH) to deal with large datasets due to its sublinear performance and theoretical guarantees on the similarity search accuracy. Surprisingly, LSH has not been considered in the crash bucketing literature. It is indeed not trivial to derive hash functions that satisfy the so-called locality-sensitive property for the most advanced crash bucketing metrics. Consequently, we study in this paper how to leverage LSH for this task. To be able to consider the most relevant metrics used in the literature, we introduce DeepLSH, a Siamese DNN architecture with an original loss function, that perfectly approximates the locality-sensitivity property even for Jaccard and Cosine metrics for which exact LSH solutions exist. We support this claim with a series of experiments on an original dataset, which we make available.

Information Technology has become a critical component in various industries, leading to an increased focus on software maintenance and monitoring. With the complexities of modern software systems, traditional maintenance approaches have become insufficient. The concept of AIOps has emerged to enhance predictive maintenance using Big Data and Machine Learning capabilities. However, exploiting AIOps requires addressing several challenges related to the complexity of data and incident management. Commercial solutions exist, but they may not be suitable for certain companies due to high costs, data governance issues, and limitations in covering private software. This paper investigates the feasibility of implementing on-premise AIOps solutions by leveraging open-source tools. We introduce a comprehensive AIOps infrastructure that we have successfully deployed in our company, and we provide the rationale behind different choices that we made to build its various components. Particularly, we provide insights into our approach and criteria for selecting a data management system and we explain its integration. Our experience can be beneficial for companies seeking to internally manage their software maintenance processes with a modern AIOps approach.

Subgroup discovery (SD) enables one to elicit patterns that strongly discriminate a class label. When it comes to numerical data, most of the existing SD approaches perform data discretizations and thus suffer from information loss. A few algorithms avoid such a loss by considering the search space of every interval pattern built on the dataset numerical values and provide an “anytime” property: at any moment, they are able to provide a result that improves over time. Given a sufficient time/memory budget, they may eventually complete an exhaustive search. However, such approaches are often intractable when dealing with high-dimensional numerical data, for instance, when extracting features from real-life multivariate time series. To overcome such limitations, we propose MonteCloPi, an approach based on a bottom-up exploration of numerical patterns with a Monte Carlo Tree Search. It enables to have a better exploration-exploitation trade-off between exploration and exploitation when sampling huge search spaces. Our extensive set of experiments proves the efficiency of MonteCloPi on high-dimensional data with hundreds of attributes. We finally discuss the actionability of discovered subgroups when looking for skill analysis from Rocket League action logs.

Among daily tasks of database administrators (DBAs), the analysis of query workloads to identify schema issues and improving performances is crucial. Although DBAs can easily pinpoint queries repeatedly causing performance issues, it remains challenging to automatically identify subsets of queries that share some properties only (a pattern) and simultaneously foster some target measures, such as execution time. Patterns are defined on combinations of query clauses, environment variables, database alerts and metrics and help answer questions like what makes SQL queries slow? What makes I/O communications high? Automatically discovering these patterns in a huge search space and providing them as hypotheses for helping to localize issues and root-causes is important in the context of explainable AI. To tackle it, we introduce an original approach rooted on Subgroup Discovery. We show how to instantiate and develop this generic data-mining framework to identify potential causes of SQL workloads issues. We believe that such data-mining technique is not trivial to apply for DBAs. As such, we also provide a visualization tool for interactive knowledge discovery. We analyse a one week workload from hundreds of databases from our company, make both the dataset and source code available, and experimentally show that insightful hypotheses can be discovered.

Competitive gaming, or esports, is now well-established and brought the game industry in a novel era. It comes with many challenges among which evaluating the level of a player, given the strategies and skills she masters. We are interested in automatically identifying the so called skillshots from game traces of Rocket League, a "soccer with rocket-powered cars" game. From a pure data point of view, each skill execution is unique and standard pattern matching may be insufficient. We propose a non trivial data-centric approach based on pattern mining and supervised learning techniques. We show through an extensive set of experiments that most of Rocket League skillshots can be efficiently detected and used for player modelling. It unveils applications for match making, supporting game commentators and learning systems among others.

Designing, selling and/or exploiting connected vertical urban farms is now receiving a lot of attention. In such farms, plants grow in controlled environments according to recipes that specify the different growth stages and instructions concerning many parameters (e.g., temperature, humidity, CO \(_{2}\) , light). During the whole process, automated systems collect measures of such parameters and, at the end, we can get some global indicator about the used recipe, e.g., its yield. Looking for innovative ideas to optimize recipes, we investigate the use of a new optimal subgroup discovery method from purely numerical data. It concerns here the computation of subsets of recipes whose labels (e.g., the yield) show an interesting distribution according to a quality measure. When considering optimization, e.g., maximizing the yield, our virtuous circle optimization framework iteratively improves recipes by sampling the discovered optimal subgroup description subspace. We provide our preliminary results about the added-value of this framework thanks to a plant growth simulator that enables inexpensive experiments.

It is extremely useful to exploit labeled datasets not only to learn models but also to improve our understanding of a domain and its available targeted classes. The so-called subgroup discovery task has been considered for a long time. It concerns the discovery of patterns or descriptions, the set of supporting objects of which have interesting properties, e.g., they characterize or discriminate a given target class. Though many subgroup discovery algorithms have been proposed for transactional data, discovering subgroups within labeled sequential data and thus searching for descriptions as sequential patterns has been much less studied. In that context, exhaustive exploration strategies can not be used for real-life applications and we have to look for heuristic approaches. We propose the algorithm SeqScout to discover interesting subgroups (w.r.t. a chosen quality measure) from labeled sequences of itemsets. This is a new sampling algorithm that mines discriminant sequential patterns using a multi-armed bandit model. It is an anytime algorithm that, for a given budget, finds a collection of local optima in the search space of descriptions and thus subgroups. It requires a light configuration and it is independent from the quality measure used for pattern scoring. Furthermore, it is fairly simple to implement. We provide qualitative and quantitative experiments on several datasets to illustrate its added-value.