The problem of information fusion from multiple data-sets acquired by multimodal sensors has drawn significant research attention over the years. In this paper, we focus on a particular problem setting consisting of a physical phenomenon or a system of interest observed by multiple sensors. We assume that all sensors measure some aspects of the system of interest with additional sensor-specific and irrelevant components. Our goal is to recover the variables relevant to the observed system and to filter out the nuisance effects of the sensor-specific variables. We propose an approach based on manifold learning, which is particularly suitable for problems with multiple modalities, since it aims to capture the intrinsic structure of the data and relies on minimal prior model knowledge. Specifically, we propose a nonlinear filtering scheme, which extracts the hidden sources of variability captured by two or more sensors, that are independent of the sensor-specific components. In addition to presenting a theoretical analysis, we demonstrate our technique on real measured data for the purpose of sleep stage assessment based on multiple, multimodal sensor measurements. We show that without prior knowledge on the different modalities and on the measured system, our method gives rise to a data-driven representation that is well correlated with the underlying sleep process and is robust to noise and sensor-specific effects. Preprint submitted to Elsevier January 16, 2017 1. Introduction Often, when measuring a phenomenon of interest that arises from a complex dynamical system, a single data acquisition method is not capable of capturing its entire complexity and characteristics, and it is usually prone to noise and interferences. Recently, due to technological advances, the use of multiple types of measurement instruments and sensors have become more and more popular; nowadays, such equipment is smaller, less expensive, and can be mounted on everyday products and devices more easily. In contrast to a single sensor, multimodal sensors may capture complementary aspects and features of the measured phenomenon, and may enable us to extract a more reliable and detailed description of the measured phenomenon. The vast progress in the acquisition of multimodal data calls for the development of analysis and processing tools, which appropriately combine data from the different sensors and handle well the inherent challenges that arise.

In today's digital loop, marketers are always changing approaches to reach an audience. Now, we're challenged to prove our content strategy works. With so much being shared, content marketers must break through the clutter. We're searching high and low for a solution to great content marketing, one that comes with a strong connection between clear metrics and creative strategy. There's a term being thrown around to rise to the occasion: content intelligence.

I've been delivering data integration solutions for decades but I only recently started working with Hadoop. McKnight's and Dolezal's book is very helpful for people using any data integration technology. The language is for executives, directors, managers, and practitioners interested in learning (or learning more) about the oft-overlooked topic of data integration. As I often tell people, "You can't do Big Data without... data." The data required for modern analytics projects is rarely stored nearby.

Data is the new language today. Data leads to insights, and insights help organizations to make actionable business decisions. However, sourcing the data and preparing it for the analysis is one of the tedious tasks organizations face these days. Analysts devote a lot of time in searching and gathering the right data. According to a research firm, analysts spend around 60 to 80 percent of their time on data preparation instead of analysis.

Data integration requires merging date from different sources, stored using technologies. Companies build a "data warehouse where aggregated data can be stored and retrieved. This is particularly useful for researchers looking to big data to aid in their investigation and corporations usually during the merging with other companies. Users can access all systems of different sources or interface of web pages but without viewing consolidated data. This organizational level requires particular applications to integrate data.

This post is a brief review of leading Data Integration tools in the market. Heavily referencing from the Gartner 2016 report and peer reviews from my circle. The data integration tool market was worth approximately $2.8 billion at the end of 2015, an increase of 10.5% from the end of 2014 [2016 Gartner Report – Data Integration Tools]. Data acquisition for business intelligence (BI), analytics and data warehousing-- extracting data from operational systems, transforming and merging that data, and delivering it to integrated data structures for analytics purposes. Data migrations/conversions -- although, traditionally addressed most often via the custom coding of conversion programs, data integration tools are increasingly addressing the data movement and transformation challenges.

According to Weisensee et al., Data warehouse architecture follows following principles: ETL process is the foundation of BI. Success and failure of BI projects depends upon ETL process. It plays a vital role to integrate and enhance the worth of data. After the extraction, cleansing and arrangement of data, it will be loaded into data warehouse. In short, ETL is the transferring process of data from data source to the target data warehouse.

Job Title: Senior Software Engineer - NLP & Machine Learning Artigen Corporation is seeking a Senior Software Engineer/Team Lead NOTE: This is a Hands on position, Software Development, Design, Framework, Programming etc. NO OPT, NO Sponsorship, No relocation assistance, Local applicants ONLY, Face-to-face interview required. Artigen is a software development company that intends to specialize in enabling Artificial Intelligence software integration with e-commerce site and back end network operations. We are looking for Senior Team Lead/Software Engineer specifically in the development of software encompassing Artificial Intelligence, Machine Learning, Natural Language Processing to develop a virtual agent for Artigen's platform of software and services for Global B2B/B2C clients/customers. The role will develop and mentor a software team dedicated to Ai and cognitive technologies, utilizing existing technologies (such as IBM Watson API, Google's Nuance, Apple's Siri, other open source upcoming platforms such as Viv) and platforms to develop and customize a platform for Artigen's own Ai platform. This role will also require hands-on complex programming in various languages, platforms and must understand and develop machine learning algorithms, data integration and manipulation.

Neuroimaging modalities such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) provide information about neurological functions in complementary spatiotemporal resolutions; therefore, fusion of these modalities is expected to provide better understanding of brain activity. In this paper, we jointly analyze fMRI and multi-channel EEG signals collected during an auditory oddball task with the goal of capturing brain activity patterns that differ between patients with schizophrenia and healthy controls. Rather than selecting a single electrode or matricizing the third-order tensor that can be naturally used to represent multi-channel EEG signals, we preserve the multi-way structure of EEG data and use a coupled matrix and tensor factorization (CMTF) model to jointly analyze fMRI and EEG signals. Our analysis reveals that (i) joint analysis of EEG and fMRI using a CMTF model can capture meaningful temporal and spatial signatures of patterns that behave differently in patients and controls, and (ii) these differences and the interpretability of the associated components increase by including multiple electrodes from frontal, motor and parietal areas, but not necessarily by including all electrodes in the analysis.