Data analysis and modeling is a complex and demanding task. While a variety of software and tools exist to cope with this problem and tame big data operations, most of these tools are either not free, and when they are, they require large amount of configuration and steep learning curve. Moreover, they provide limited functionalities. In this paper we propose Polly, an online data analysis and modeling open-source tool that is intuitive to use and can be used with minimal or no configuration. Users can use Polly to rapidly integrate, analyze their data, prototype and test their novel methodologies. Polly can be used also as an educational tool. Users can use Polly to upload or connect to their structured data sources, load the required data into our system and perform various data processing tasks. Examples of such operations include data cleaning, data pre-processing, attribute encoding, regression and classification analysis.

Location: US, Central Region OR Toronto, Canada Talend is a 600 employee, recent IPO, big data integration software company with deep open source roots. Well-funded, with over $100 million raised to date and continued rapid growth, Talend is the second largest independent open source company in the world. We are hiring Pre Sales Engineers to continue to build a proactive, customer-facing organization that ensures customers are getting value from Talend's products and solutions. We are seeking Engineers to join the sales team and support the increasing demand from our direct sales. Our portfolio of products has expanded from purely Data Integration to include Data Quality (DQ), Master Data Management (MDM), Enterprise Service Bus (ESB) and Big Data.

Medical ML/DL system shall facilitate a deep understanding of the underlying healthcare task, which (in most cases) can only be achieved by utilising other forms of patients data. For example, radiology is not all about clinical imaging. Other patient EMR data is crucial for radiologists to derive the precise conclusion for an imaging study. This calls for the integration and data exchange between all healthcare systems. Despite extensive research on data exchange standards for healthcare, there is a huge ignorance in following those standards in healthcare IT systems which broadly affects the quality and efficacy of healthcare data, accumulated through these systems.

Towards a deep understanding of these languages in model theory, we establish model-theoretic characterizations for a number of existential rule languages such as (disjunctive) embedded dependencies, tuple-generating dependencies (TGDs), (frontier-)guarded TGDs and linear TGDs. All these characterizations hold for arbitrary structures, and most of them also work on the class of finite structures. As a natural application of these characterizations, complexity bounds for the rewritability of above languages are also identified. 1 Introduction Existential rule languages, a family of languages that extend Datalog by allowing existential quantifiers in the rule head, had been initially introduced in databases in 1970s to specify the semantics of data stored in a database [ Abiteboul et al., 1995] . Since then, existential rule languages such as tuple-generating dependencies (TGDs), embedded dependencies and equality-generating dependencies have been extensively studied. These language have been recently rediscovered as languages for data exchange [ Fagin et al., 2005 ], data integration [ Lenzerini, 2002 ] and ontology-mediated query answering [ Cal ı et al., 2010 ] .

Ever since the early days of reporting, data warehousing, and analytics, there has been a need for data integration for many reasons. However, if you fast forward 30 years to today, you will see the traditional extract/transform/load (ETL) approach is insufficient to enable real-time predictive and prescriptive analytics. This checklist explores ideas for determining where traditional approaches to data integration are impeding modern analytics and will guide the reader in ways to modernize. Your e-mail address is used to communicate with you about your registration, related products and services, and offers from select vendors.

-- T he pivotal key for the success of manufacturing enterprises is sustainable and innovative product design and development. In collaborative design, stakehol ders are heterogeneously distributed chain - like . Due to the growing volume of data and knowledge, an effective management of the knowledge acquired in the product design and development is one of the key challenges facing most manufacturing enterprises. Opportunities for improving efficiency and performance of IT - based product design applications through centralization of resources such as knowledge and computation have increased in the last few years with maturation of technologies such as SOA, virtualization, grid computing, and /or cloud computing. The main focus of this paper is the concept of ongoing research in providing the knowledge integration service for collaborative product design and development using cloud computing infra structure . P otential s of the cloud computing to support the Knowledge integration functionalities as a Service by providing functionalities such as knowledge mapping, merging, searching, and transferring in product design procedure are described in this paper . Proposed knowledge integration services support users by giving real - time access to knowledge resources. The framework has the advantage of availability, efficiency, cost reduction, less time to result, and scalability . Changes made during the early design stage do not cause the significant increase in costs, while during the production stage, sharp increase in costs will occur since many blueprints, design documents or components would require re - work and re - design [ 5 ] . Today's research is focused on optimising the development methodologies to enable shorter time, lower costs and higher quality of the systems [ 2 ] . The pivotal key for the success of manufacturing enterprises is sustainable and innovative product design and development . In order to achieve this goal, it is required to have a real and deep knowledge of former and current procedures in the manufacturing enterprise [4] and future needs as well as customer feedback s and various stages of production cha in activities. Realization of an efficient knowledge transfer between different stakeholders of product development process such as linking customers and suppliers proactively throughout the entire value chain, and collaborating across boundaries in distri buted enterprise s is reinforcing this trend.

Fusion is a common tool for the analysis and utilization of available datasets and so an essential part of data mining and machine learning processes. However, a clear definition of the type of fusion is not always provided due to inconsistent literature. In the following, the process of fusion is defined depending on the fusion components and the abstraction level on which the fusion occurs. The focus in the first part of the paper at hand is on the clear definition of the terminology and the development of an appropriate ontology of the fusion components and the fusion level. In the second part, common fusion techniques are presented.

Rising number of contact centers and necessity for compliance and risk management across several verticals have led the companies to invent solutions in speech analytics which will aid companies to comprehend the changing necessities of customers. Several organizations functioning in diverse industrial domains have been evolving interests for the transcription and analyzing of customers and structural media and uptake rational decisions for the management of business and consumers with the help of speech and text intelligence. This is the main factor that is responsible for the growth of the speech analytics market and a protuberant driving factor in the growing demands for speech analytics in several industrial applications. This rising demand can also be accredited to the burdens on businesses for safeguarding their rational assets for improving agility and competence in business operations via the all-embracing insights quarried in the Voice of Customer (VoC). Speech analytics is used in sectors such as customer experience management, agent performance, business processes, compliance and risk management, and market intelligence.

Rising number of contact centers and necessity for compliance and risk management across several verticals have led the companies to invent solutions in speech analytics which will aid companies to comprehend the changing necessities of customers. Several organizations functioning in diverse industrial domains have been evolving interests for the transcription and analyzing of customers and structural media and uptake rational decisions for the management of business and consumers with the help of speech and text intelligence. This is the main factor that is responsible for the growth of the speech analytics market and a protuberant driving factor in the growing demands for speech analytics in several industrial applications. This rising demand can also be accredited to the burdens on businesses for safeguarding their rational assets for improving agility and competence in business operations via the all-embracing insights quarried in the Voice of Customer (VoC). Speech analytics is used in sectors such as customer experience management, agent performance, business processes, compliance and risk management, and market intelligence.

Description The Data Engineering team at Intuit's Small Business Group (SBG) is looking for a Senior Data Engineer – QE with a winning track record in Big Data, Data Warehousing, Visualization and Data Web Services. Responsibilities: Work with Data Engineers, Product Managers and Data Scientists to identify datasets needed for deep customer insights and for building operational propensity models. Work with data ingestion engineers to bring required source datasets into the data warehouse. Test ETL code to populate the dimensional model. Work with BI developers to ensure that the data warehouse is providing the required data and the required performance.