1,721,092 research outputs found
A Cost Model for SPARK SQL
Full text linkIn this paper we propose a novel cost model for Spark SQL. The cost model covers the class of Generalized Projection, Selection, Join (GPSJ) queries. The cost model keeps into account the network and IO costs as well as the most relevant CPU costs. The execution cost is computed starting from a physical plan produced by Spark. The set of operations adopted by Spark when executing a GPSJ query are analytically modeled based on the cluster and application parameters, together with a set of database statistics. Experimental results carried out on three benchmarks and on two clusters of different sizes and with different computation features show that our model can estimate the actual execution time with about the 20% of errors on the average. Such an accuracy is good enough to let the system choose the most effective plan even when the execution time differences are limited. The error can be reduced to 14%, if the analytic model is coupled with our straggler handling strategy
A Model-Driven Approach to Automate Data Visualization in Big Data Analytics
Full text linkIn big data analytics, advanced analytic techniques operate on big data sets aimed at complementing the role of traditional OLAP for decision making. To enable companies to take benefit of these techniques despite the lack of in-house technical skills, the H2020 TOREADOR Project adopts a model-driven architecture for streamlining analysis processes, from data preparation to their visualization. In this paper we propose a new approach named SkyViz focused on the visualization area, in particular on (i) how to specify the user's objectives and describe the dataset to be visualized, (ii) how to translate this specification into a platform-independent visualization type, and (iii) how to concretely implement this visualization type on the target execution platform. To support step (i) we define a visualization context based on seven prioritizable coordinates for assessing the user's objectives and conceptually describing the data to be visualized. To automate step (ii) we propose a skyline-based technique that translates a visualization context into a set of most-suitable visualization types. Finally, to automate step (iii) we propose a skyline-based technique that, with reference to a specific platform, finds the best bindings between the columns of the dataset and the graphical coordinates used by the visualization type chosen by the user. SkyViz can be transparently extended to include more visualization types on the one hand, more visualization coordinates on the other. The paper is completed by an evaluation of SkyViz based on a case study excerpted from the pilot applications of the TOREADOR Project
SparkTune: tuning Spark SQL through query cost modeling
No full textWe demonstrate SparkTune, a tool that supports the evaluation and tuning of Spark SQL workloads from multiple perspectives. Unlike Spark SQL's optimizer, which mainly relies on a rule-based model, SparkTune adopts a cost-based model for SQL queries; this enables the accurate estimation of execution times and the identification of cost and complexity factors in a user-defined workload. The estimate is based on the cluster configuration, the database statistics (both automatically retrieved by the tool) and the resources allocated to the workload. Thus, for any given cluster, database and workload, SparkTune is able to identify the best cluster configuration to run the workload, to estimate the price to run it on a cloud platform while evaluating the performance/price trade-off, and more. SparkTune turns the cluster tuning efforts from manual and qualitative to automatic, optimized and quantitative
Describing and Assessing Cubes Through Intentional Analytics
Full text linkThe Intentional Analytics Model (IAM) has been envisioned as a way to tightly couple OLAP and analytics by (i) letting users explore multidimensional cubes stating their intentions, and (ii) returning multidimensional data coupled with knowledge insights in the form of annotations of subsets of data. Goal of this demonstration is to showcase the IAM approach using a notebook where the user can create a data exploration session by writing describe and assess statements, whose results are displayed by combining tabular data and charts so as to bring the highlights discovered to the user's attention. The demonstration plan will show the effectiveness of the IAM approach in supporting data exploration and analysis and its added value as compared to a traditional OLAP session by proposing two scenarios with guided interaction and letting users run custom sessions
Conversational OLAP in Action
Full text linkThe democratization of data access and the adoption of OLAP in scenarios requiring hand-free interfaces push towards the creation of smart OLAP interfaces. In this demonstration we present COOL, a tool supporting natural language COnversational OLap sessions. COOL interprets and translates a natural language dialogue into an OLAP session that starts with a GPSJ (Generalized Projection, Selection and Join) query. The interpretation relies on a formal grammar and a knowledge base storing metadata from a multidimensional cube. COOL is portable, robust, and requires minimal user intervention. It adopts an n-gram based model and a string similarity function to match known entities in the natural language description. In case of incomplete text description, COOL can obtain the correct query either through automatic inference or through interactions with the user to disambiguate the text. The goal of the demonstration is to let the audience evaluate the usability of COOL and its capabilities in assisting query formulation and ambiguity/error resolution
A-BI+: A Framework for Augmented Business Intelligence
Full text linkAugmented reality allows users to superimpose digital information (typically, of operational type) upon real-world objects. The synergy of analytical frameworks and augmented reality opens the door to a new wave of situated analytics, in which users within a physical environment are provided with immersive analyses of local contextual data. In this paper, we propose an approach named A-BI+ (Augmented Business Intelligence) that, based on the sensed augmented context (provided by wearable and smart devices), proposes a set of relevant analytical queries to the user. This is done by relying on a mapping between the objects that can be recognized by the devices and the elements of the enterprise multidimensional cubes, and also by taking into account the queries preferred by users during previous interactions that occurred in similar contexts. A set of experimental tests evaluates the proposed approach in terms of efficiency, effectiveness, and user satisfaction
Summarization and Visualization of Multi-Level and Multi-Dimensional Itemsets
Full text linkFrequent itemset (FI) mining aims at discovering relevant patterns from sets of transactions. In this work we focus on multi-level and multi-dimensional data, which provide a rich description of subjects through multiple features each at different levels of detail. Summarization of FIs has been only marginally addressed so far with specific reference to multi-level and multi-dimensional FIs. In this paper we fill this gap by proposing SUSHI, a framework for summarizing and visually exploring multi-level and multi-dimensional FIs. Specifically, SUSHI is based on (i) a similarity function for FIs which takes into account both their extensional (support-based) and intensional (feature-based) natures; (ii) theoretical results concerning antimonotonicity of support and similarity in multi-level settings, which allow us to propose an efficient clustering algorithm to generate hierarchical summaries; and (iii) two integrated approaches to summary visualization and exploration: a graph-based one, which highlights inter-cluster relationships, and a tree-based one, which emphasizes the relationships between the representative of each cluster and the other FIs in that cluster. SUSHI is evaluated using both a real and a synthetic dataset in terms of effectiveness, efficiency, and understandability of the summary, with reference to three different strategies for choosing cluster representatives. Overall, SUSHI shows to outperform previous approaches and to be a valuable tool to expedite the analysis of FIs. Besides, one of the three strategies for choosing cluster representatives shows to be the most effective one
Augmented Business Intelligence
No full textAugmented reality allows users to superimpose digital information (typically, of operational type) upon real world entities. The synergy of analytical frameworks and augmented reality opens the door to a new wave of situated OLAP, in which users within a physical environment are provided with immersive analyses of local contextual data. In this paper we propose an approach that, based on the sensed augmented context (provided by wearable and smart devices), proposes a set of relevant analytical queries to the user. This is done by relying on a mapping between the entities that can be recognized by the devices and the elements of the enterprise data, and also taking into account the queries preferred by users during previous interactions that occurred in similar contexts. A set of experimental tests evaluates the proposed approach in terms of efficiency and effectiveness
An Active Learning Approach to Build Adaptive Cost Models for Web Services
No full textDelivering accurate estimates of query costs in web services is important in different contexts, e.g., to measure their Quality of Service. However, building a reliable cost model is difficult as (i) a web service is a black box often hiding a complex computation, (ii) a call to the same service can yield completely different costs by simply changing a parameter value, and (iii) execution costs can drift with time. In this paper we propose Tiresias, an approach that, given a web service exposing an interface with a fixed number of parameters, initializes and actively adapts a model to accurately predict query costs. The cost model is represented by a regression tree trained through two interleaved querying cycles: a passive one, where the costs measured for user-generated queries are used to update the tree, and an active one, where the service is probed through system-generated queries to cope with drifts in the cost function. Tiresias is finally evaluated in terms of effectiveness and efficiency through a set of experimental tests performed on both real and synthetic datasets
Approximate OLAP of Document-Oriented Databases: a Variety-Aware Approach
Full text linkSchemaless databases, and document-oriented databases in particular, are preferred to relational ones for storing heterogeneous data with variable schemas and structural forms. However, the absence of a unique schema adds complexity to analytical applications, in which a single analysis often involves large sets of data with different schemas. In this paper we propose an original approach to OLAP on collections stored in document-oriented databases. The basic idea is to stop fighting against schema variety and welcome it as an inherent source of information wealth in schemaless sources. Our approach builds on four stages: schema extraction, schema integration, FD enrichment, and querying; these stages are discussed in detail in the paper. To make users aware of the impact of schema variety, we propose a set of indicators inspired by the definition of attribute density. Finally, we experimentally evaluate our approach in terms of efficiency and effectiveness
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