This paper showcases Time2Feat, an end-to-end machine learning system for Multivariate Time Series (MTS) clustering. The system relies on interpretable inter-signal and intra-signal features extracted from the time series. Then, a dimensionality reduction technique is applied to select a subset of features that retain most of the information, thus enhancing the interpretability of the results. In addition, the system enables domain specialists to semi-supervise the process by submitting a small collection of MTS with a target cluster. This process further improves both accuracy and interpretability, by reducing the number of features used by the clustering process. The demonstration shows the application of Time2Feat to various MTS datasets, by creating clusters from MTS datasets of interest, experimenting with different settings and using the approach capabilities to interpret the clusters generated.

Clustering multivariate time series is a critical task in many real-world applications involving multiple signals and sensors. Existing systems aim to maximize effectiveness, efficiency and scalability, but fail to guarantee the interpretability of the results. This hinders their application in critical real scenarios where human comprehension of algorithmic behavior is required. This paper introduces Time2Feat, an end-to-end machine learning system for multivariate time series (MTS) clustering. The system relies on inter-signal and intra-signal interpretable features extracted from the time series. Then, a dimensionality reduction technique is applied to select a subset of features that retain most of the information, thus enhancing the interpretability of the results. In addition, domain experts can semi-supervise the process, by providing a small amount of MTS with a target cluster. This process further improves both accuracy and interpretability, narrowing down the number of features used by the clustering process. We demonstrate the effectiveness, interpretability, efficiency, and robustness of Time2Feat through experiments on eighteen benchmarking time series datasets, comparing them with state-of-the-art MTS clustering methods.

State-of-the-art Entity Matching (EM) approaches rely on transformer architectures, such as BERT , for generating highly contex-tualized embeddings of terms. The embeddings are then used to predict whether pairs of entity descriptions refer to the same real-world entity. BERT-based EM models demonstrated to be effective, but act as black-boxes for the users, who have limited insight into the motivations behind their decisions. In this paper, we perform a multi-facet analysis of the components of pre-trained and fine-tuned BERT architectures applied to an EM task. The main findings resulting from our extensive experimental evaluation are (1) the fine-tuning process applied to the EM task mainly modifies the last layers of the BERT components, but in a different way on tokens belonging to descriptions of matching / non-matching entities; (2) the special structure of the EM datasets, where records are pairs of entity descriptions is recognized by BERT; (3) the pair-wise semantic similarity of tokens is not a key knowledge exploited by BERT-based EM models.

State-of-the-art approaches model Entity Matching (EM) as a binary classification problem, where Machine (ML) or Deep Learning (DL) based techniques are applied to evaluate if descriptions of pairs of entities refer to the same real-world instance. Despite these approaches have experimentally demonstrated to achieve high effectiveness, their adoption in real scenarios is limited by the lack of interpretability of their behavior.

The state of the art approaches for performing Entity Matching (EM) rely on machine & deep learning models for inferring pairs of matching / non-matching entities.Although the experimental evaluations demonstrate that these approaches are effective, their adoption in real scenarios is limited by the fact that they are difficult to interpret.Explainable AI systems have been recently proposed for complementing deep learning approaches.Their application to the scenario offered by EM is still new and requires to address the specificity of this task, characterized by particular dataset schemas, describing a pair of entities, and imbalanced classes.This paper introduces Landmark Explanation, a generic and extensible framework that extends the capabilities of a post-hoc perturbation-based explainer over the EM scenario.Landmark Explanation generates perturbations that take advantage of the particular schemas of the EM datasets, thus generating explanations more accurate and more interesting for the users than the ones generated by competing approaches.

The paper studies the application of automated machine learning approaches (AutoML) for addressing the problem of Entity Matching (EM).This would make the existing, highly effective, Machine Learning (ML) and Deep Learning based approaches for EM usable also by non-expert users, who do not have the expertise to train and tune such complex systems.Our experiments show that the direct application of AutoML systems to this scenario does not provide high quality results.To address this issue, we introduce a new component, the EM adapter, to be pipelined with standard AutoML systems, that preprocesses the EM datasets to make them usable by automated approaches.The experimental evaluation shows that our proposal obtains the same effectiveness as the state-of-the-art EM systems, but it does not require any skill on ML to tune it. INTRODUCTIONMachine Learning (ML) has significantly advanced over the past ten years [1].On one side, the research on Big Data let emerge new challenges and made available scenarios and datasets where to experiment and improve ML techniques.On the other side, the increase of computer processing power, thanks in particular to the use of graphic processing units, enabled ML approaches running in commodity hardware.This led to the development of new ML algorithms and their implementations through frameworks and libraries is extensive and growing [17].Thus the ML technology moved from an R&D phase, for the exclusive use of specialized laboratories, to a mature phase where it can be adopted in business applications.Mature technologies have to be easy to use for both nonexperts and professionals.One of the main bottlenecks towards a large use of the ML technology is related to the configuration of the systems, where experts are typically needed to set the large number of hyper-parameters.Furthermore, the selection of the algorithm that best performs in a given ML task is based on an experimental evaluation in which the performances of competing approaches are compared.This requires a time-consuming and expensive iterative process in which multiple alternative solutions are tested until an optimal result is achieved.To address these issues, automated machine learning (AutoML) tools have been proposed.These are user-friendly and easy-touse systems that provide a unified interface for the automatic selection of the most appropriate ML model/algorithm for a given task and its automatic configuration.Some examples are Auto-WEKA [12], AutoSklearn [9], AutoGluon [8], Auto-Keras [11], H20 AutoML [10], and many other.This paper analyzes the application of AutoML systems to Entity Matching (EM), i.e. the task of identifying which records in a dataset refer to the same real-world entity [5].Applications

Inference of Machine Learning (ML) models, i.e. the process of obtaining predictions from trained models, is often an overlooked problem. Model inference is however one of the main contributors of both technical debt in ML applications and infrastructure complexity. MASQ is a framework able to run inference of ML models directly on DBMSs. MASQ not only averts expensive data movements for those predictive scenarios where data resides on a database, but it also naturally exploits all the "Enterprise-grade" features such as governance, security and auditability which make DBMSs the cornerstone of many businesses. MASQ compiles trained models and ML pipelines implemented in scikit-learn directly into standard SQL: no UDFs nor vendor-specific syntax are used, and therefore queries can be readily executed on any DBMS. In this demo, we will showcase MASQ's capabilities through a GUI allowing attendees to: (1) train ML pipelines composed of data featurizers and ML models; (2) compile the trained pipelines into SQL, and deploy them on different DBMSs (MySQL and SQLServer in the demo); and (3) compare the related performance under different configurations (e.g., the original pipeline on the ML framework against the SQL implementations).

Evaluation of the quality of data integration processes is usually performed via manual onerous data inspections. This task is particularly heavy in real business scenarios, where the large amount of data makes checking all the tuples infeasible and the frequent updates, i.e. changes in the sources and/or new sources, impose to repeat the evaluation over and over. Our idea is to address this issue by providing the experts with an unsupervised measure, based on word frequencies, which quantifies how much a dataset is representative of another dataset, giving an indication of how good is the integration process and whether deviations are happening and a manual inspection is needed. We also conducted some preliminary experiments, using shared datasets, that show the effectiveness of the proposed measures in typical data integration scenarios.