A Holistic Approach to OLAP Sessions Composition: The Falseto Experience

International audienceOLAP is the main paradigm for flexible and effective exploration of multidimensional cubes in data warehouses. During an OLAP session the user analyzes the results of a query and determines a new query that will give her a better understanding of information. Given the huge size of the data space, this exploration process is often tedious and may leave the user disoriented and frustrated. This paper presents an OLAP tool 1 named Falseto (Former AnalyticaL Sessions for lEss Tedious Olap), that is meant to assist query and session composition, by letting the user summarize, browse, query, and reuse former analytical sessions. Falseto's implementation on top of a formal framework is detailed. We also report the experiments we run to obtain and analyze real OLAP sessions and assess Falseto with them. Finally, we discuss how Falseto can be seen as a starting point for bridging OLAP with exploratory search, a search paradigm centered on the user and the evolution of her knowledge

Why Should I Choose You? AutoXAI: A Framework for Selecting and Tuning eXplainable AI Solutions

In recent years, a large number of XAI (eXplainable Artificial Intelligence) solutions have been proposed to explain existing ML (Machine Learning) models or to create interpretable ML models. Evaluation measures have recently been proposed and it is now possible to compare these XAI solutions. However, selecting the most relevant XAI solution among all this diversity is still a tedious task, especially when meeting specific needs and constraints. In this paper, we propose AutoXAI, a framework that recommends the best XAI solution and its hyperparameters according to specific XAI evaluation metrics while considering the user's context (dataset, ML model, XAI needs and constraints). It adapts approaches from context-aware recommender systems and strategies of optimization and evaluation from AutoML (Automated Machine Learning). We apply AutoXAI to two use cases, and show that it recommends XAI solutions adapted to the user's needs with the best hyperparameters matching the user's constraints.Comment: 16 pages, 7 figures, to be published in CIKM202

Weather Data Visualization and Analytical Platform

This article aims to present a web-based interactive visualization and analytical platform for weather data in Armenia by integrating the three existing infrastructures for observational data, numerical weather prediction, and satellite image processing. The weather data used in the platform consists of near-surface atmospheric elements including air temperature, pressure, relative humidity, wind and precipitation. The visualization and analytical platform has been implemented for 2-m surface temperature. The platform gives Armenian State Hydrometeorological and Monitoring Service analytical capabilities to analyze the in-situ observations, model and satellite image data per station and region for a given period

Edge-centric queries stream management based on an ensemble model

The Internet of things (IoT) involves numerous devices that can interact with each other or with their environment to collect and process data. The collected data streams are guided to the cloud for further processing and the production of analytics. However, any processing in the cloud, even if it is supported by improved computational resources, suffers from an increased latency. The data should travel to the cloud infrastructure as well as the provided analytics back to end users or devices. For minimizing the latency, we can perform data processing at the edge of the network, i.e., at the edge nodes. The aim is to deliver analytics and build knowledge close to end users and devices minimizing the required time for realizing responses. Edge nodes are transformed into distributed processing points where analytics queries can be served. In this paper, we deal with the problem of allocating queries, defined for producing knowledge, to a number of edge nodes. The aim is to further reduce the latency by allocating queries to nodes that exhibit low load (the current and the estimated); thus, they can provide the final response in the minimum time. However, before the allocation, we should decide the computational burden that a query will cause. The allocation is concluded by the assistance of an ensemble similarity scheme responsible to deliver the complexity class for each query. The complexity class, thus, can be matched against the current load of every edge node. We discuss our scheme, and through a large set of simulations and the adoption of benchmarking queries, we reveal the potentials of the proposed model supported by numerical results

Recommandation de sessions OLAP, basé sur des mesures de similarités

L’OLAP (On-Line Analytical Processing) est le paradigme principal pour accéder aux données multidimensionnelles dans les entrepôts de données. Pour obtenir une haute expressivité d’interrogation, malgré un petit effort de formulation de la requête, OLAP fournit un ensemble d’opérations (comme drill-down et slice-and-dice ) qui transforment une requête multidimensionnelle en une autre, de sorte que les requêtes OLAP sont normalement formulées sous la forme de séquences appelées Sessions OLAP. Lors d’une session OLAP l’utilisateur analyse les résultats d’une requête et, selon les données spécifiques qu’il voit, applique une seule opération afin de créer une nouvelle requête qui lui donnera une meilleure compréhension de l’information. Les séquences de requêtes qui en résultent sont fortement liées à l’utilisateur courant, le phénomène analysé, et les données. Alors qu’il est universellement reconnu que les outils OLAP ont un rôle clé dans l’exploration souple et efficace des cubes multidimensionnels dans les entrepôts de données, il est aussi communément admis que le nombre important d’agrégations et sélections possibles, qui peuvent être exploités sur des données, peut désorienter l’expérience utilisateur.OLAP (On-Line Analytical Processing) is the main paradigm for accessing multidimensional data in data warehouses. To obtain high querying expressiveness despite a small query formulation effort, OLAP provides a set of operations (such as drill-down and slice-and-dice) that transform one multidimensional query into another, so that OLAP queries are normally formulated in the form of sequences called OLAP sessions. During an OLAP session the user analyzes the results of a query and, depending on the specific data she sees, applies one operation to determine a new query that will give her a better understanding of information. The resulting sequences of queries are strongly related to the issuing user, to the analyzed phenomenon, and to the current data. While it is universally recognized that OLAP tools have a key role in supporting flexible and effective exploration of multidimensional cubes in data warehouses, it is also commonly agreed that the huge number of possible aggregations and selections that can be operated on data may make the user experience disorientating

Exploration de données et journalisme : vers la spécification de narrations journalistiques

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Summarizing former sessions for user-centric OLAP

International audienceWe propose a framework for summarizing former analyses to assist the user exploring a data cube. In this framework, simple operators are used for automatically summarizing log files consisted of sequences of unevaluated OLAP queries. We provide a simple implementation of the framework for summarizing logs of OLAP queries, and we test it with respect to a query personalization technique based on mining a query log

A framework for summarizing a log of OLAP queries

International audienceLeveraging query logs benefits the users analyzing large data warehouses. But so far nothing exists to allow the user to have concise and usable representation of what is in the log. In this paper, we propose a framework for summarizing OLAP query logs. This framework is based on the idea that a query can summarize another query and that a log can summarize another log. It includes a simple language to declaratively specify a summary, a measure to assess the quality of a summary and an algorithm for automatically computing a good quality summary of a query log

Explaining single predictions : a faster method

International audienceMachine learning has proven increasingly essential in manyfields. Yet, a lot obstacles still hinder its use by non-experts. The lack oftrust in the results obtained is foremost among them, and has inspiredseveral explanatory approaches in the literature. In this paper, we areinvestigating the domain of single prediction explanation. This is per-formed by providing the user a detailed explanation of the attribute'sinfluence on each single predicted instance, related to a particular ma-chine learning model. A lot of possible explanation methods have beendeveloped recently. Although, these approaches often require an impor-tant computation time in order to be efficient. That is why we are inves-tigating about new proposals of explanation methods, aiming to increasetime performances, for a small loss in accuracy

A framework for summarizing OLAP query logs

International audienceLeveraging query logs benefits the users analyzing large data warehouses. But so far nothing exists to allow the user to have concise and usable representation of what is in the log. In this paper, we propose a framework for summarizing OLAP query logs. This framework is based on the idea that a query can summarize another query and that a log can summarize another log. It includes a simple language to declaratively specify a summary, a measure to assess the quality of a summary and an algorithm for automatically computing a good quality summary of a query log