Supporting Image Search with Tag Clouds: A Preliminary Approach

Algorithms and techniques for searching in collections of data address a challenging task, since they have to bridge the gap between the ways in which users express their interests, through natural language expressions or keywords, and the ways in which data is represented and indexed.When the collections of data include images, the task becomes harder, mainly for two reasons. From one side the user expresses his needs through one medium (text) and he will obtain results via another medium (some images). From the other side, it can be difficult for a user to understand the results retrieved; that is why a particular image is part of the result set. In this case, some techniques for analyzing the query results and giving to the users some insight into the content retrieved are needed. In this paper, we propose to address this problem by coupling the image result set with a tag cloud of words describing it. Some techniques for building the tag cloud are introduced and two application scenarios are discussed

BLAST: a Loosely Schema-aware Meta-blocking Approach for Entity Resolution

Identifying records that refer to the same entity is a fundamental step for data integration. Since it is prohibitively expensive to compare every pair of records, blocking techniques are typically employed to reduce the complexity of this task. These techniques partition records into blocks and limit the comparison to records co-occurring in a block. Generally, to deal with highly heterogeneous and noisy data (e.g. semi-structured data of the Web), these techniques rely on redundancy to reduce the chance of missing matches. Meta-blocking is the task of restructuring blocks generated by redundancy-based blocking techniques, removing superfluous comparisons. Existing meta-blocking approaches rely exclusively on schema-agnostic features. In this paper, we demonstrate how “loose” schema information (i.e., statistics collected directly from the data) can be exploited to enhance the quality of the blocks in a holistic loosely schema-aware (meta-)blocking approach that can be used to speed up your favorite Entity Resolution algorithm. We call it Blast (Blocking with Loosely-Aware Schema Techniques). We show how Blast can automatically extract this loose information by adopting a LSH-based step for e ciently scaling to large datasets. We experimentally demonstrate, on real-world datasets, how Blast outperforms the state-of-the-art unsupervised meta-blocking approaches, and, in many cases, also the supervised one

Giant hydatid lung cyst in non-endemic area

Abstract Echinococcosis is a parasitic zoonosis in which humans are involved as an intermediate host. It is a very common pathology in the areas of Eastern Europe and Asia, where the main activity is represented by pastoralism and animal breeding. In humans, the most frequently affected sites are the liver (60–70%) and lungs (20–30%), manifesting themselves with symptoms such as abdominal pain, coughing, dyspnea, hemoptysis, up to cases of anaphylactic shock. In Italy it is not a common disease with an incidence of 2:100.000 inhabitants, and it's extremely rare in pediatric population. In this work we presented a case of a 11-years male patient came for a persistent cough, to whom was found a giant cystic mass in the thorax. Serological tests confirmed the diagnosis of echinococcosis. Patient underwent to thoracotomy to remove the cyst and he begun the antiparasitic therapy. Post-operative outcome was good and the patient is, until now, free of problems. Echinococcosis is a challenging problem for a pediatric surgeon, especially in non-endemic area, not only because it is not a common disease in pediatric population, but also for the surgical management that can presents some difficulties and complications in the post-operative course

Entity Resolution On-Demand

Entity Resolution (ER) aims to identify and merge records that refer to the same real-world entity. ER is typically employed as an expensive cleaning step on the entire data before consuming it. Yet, determining which entities are useful once cleaned depends solely on the user's application, which may need only a fraction of them. For instance, when dealing with Web data, we would like to be able to filter the entities of interest gathered from multiple sources without cleaning the entire, continuously-growing data. Similarly, when querying data lakes, we want to transform data on-demand and return the results in a timely manner---a fundamental requirement of ELT (Extract-Load-Transform) pipelines. We propose BrewER, a framework to evaluate SQL SP queries on dirty data while progressively returning results as if they were issued on cleaned data. BrewER tries to focus the cleaning effort on one entity at a time, following an ORDER BY predicate. Thus, it inherently supports top-k and stop-and-resume execution. For a wide range of applications, a significant amount of resources can be saved. We exhaustively evaluate and show the efficacy of BrewER on four real-world datasets

SparkER: Scaling Entity Resolution in Spark

We present SparkER, an ER tool that can scale practitioners’ favorite ER algorithms. SparkER has been devised to take full ad- vantage of parallel and distributed computation as well (running on top of Apache Spark). The first SparkER version was focused on the blocking step and implements both schema-agnostic and Blast meta-blocking approaches (i.e. the state-of-the-art ones); a GUI for SparkER, to let non-expert users to use it in an unsupervised mode, was developed. The new version of SparkER to be shown in this demo, extends significantly the tool. Entity matching and Entity Clustering modules have been added. Moreover, in addition to the completely unsupervised mode of the first version, a supervised mode has been added. The user can be assisted in supervising the entire process and in injecting his knowledge in order to achieve the best result. During the demonstration, attendees will be shown how SparkER can significantly help in devising and debugging ER algorithms

Entity Resolution On-Demand for Querying Dirty Datasets

Entity Resolution (ER) is the process of identifying and merging records that refer to the same real-world entity. ER is usually applied as an expensive cleaning step on the entire data before consuming it, yet the relevance of cleaned entities ultimately depends on the user’s specific application, which may only require a small portion of the entities. We introduce BrewER, a framework designed to evaluate SQL SP queries on unclean data while progressively providing results as if they were obtained from cleaned data. BrewER aims at cleaning a single entity at a time, adhering to an ORDER BY predicate, thus it inherently supports top-k queries and stop-and-resume execution. This approach can save a significant amount of resources for various applications. BrewER has been implemented as an open-source Python library and can be seamlessly employed with existing ER tools and algorithms. We thoroughly demonstrated its efficiency through its evaluation on four real-world datasets

bigbench workload executed by using apache flink

Abstract Many of the challenges that have to be faced in Industry 4.0 involve the management and analysis of huge amount of data (e.g. sensor data management and machine-fault prediction in industrial manufacturing, web-logs analysis in e-commerce). To handle the so-called Big Data management and analysis, a plethora of frameworks has been proposed in the last decade. Many of them are focusing on the parallel processing paradigm, such as MapReduce , Apache Hive , Apache Flink . However, in this jungle of frameworks, the performance evaluation of these technologies is not a trivial task, and strictly depends on the application requirements. The scope of this paper is to compare two of the most employed and promising frameworks to manage big data: Apache Flink and Apache Hive , which are general purpose distributed platforms under the umbrella of the Apache Software Foundation. To evaluate these two frameworks we use the benchmark BigBench , developed for Apache Hive . We re-implemented the most significant queries of Apache Hive BigBench to make them work on Apache Flink , in order to be able to compare the results of the same queries executed on both frameworks. Our results show that Apache Flink , if it is configured well, is able to outperform Apache Hive

BigDedup: a Big Data Integration toolkit for Duplicate Detection in Industrial Scenarios

Duplicate detection aims to identify different records in data sources that refers to the same real-world entity. It is a fundamental task for: item catalogs fusion, customer databases integration, fraud detection, and more. In this work we present BigDedup, a toolkit able to detect duplicate records on Big Data sources in an efficient manner. BigDedup makes available the state-of-the-art duplicate detection techniques on Apache Spark, a modern framework for distributed computing in Big Data scenarios. It can be used in two different ways: (i) through a simple graphic interface that permit the user to process structured and unstructured data in a fast and effective way; (ii) as a library that provides different components that can be easily extended and customized. In the paper we show how to use BigDedup and its usefulness through some industrial examples