Data quality assessment and data cleaning are context-dependent activities. Motivated by this observation, we propose the Ontological Multidimensional Data Model (OMD model), which can be used to model and represent contexts as logic-based ontologies. The data under assessment is mapped into the context, for additional analysis, processing, and quality data extraction. The resulting contexts allow for the representation of dimensions, and multidimensional data quality assessment becomes possible. At the core of a multidimensional context we include a generalized multidimensional data model and a Datalog+/- ontology with provably good properties in terms of query answering. These main components are used to represent dimension hierarchies, dimensional constraints, dimensional rules, and define predicates for quality data specification. Query answering relies upon and triggers navigation through dimension hierarchies, and becomes the basic tool for the extraction of quality data. The OMD model is interesting per se, beyond applications to data quality. It allows for a logic-based, and computationally tractable representation of multidimensional data, extending previous multidimensional data models with additional expressive power and functionalities.

Despite large incentives, correctness in software remains an elusive goal. Declarative programming techniques, where algorithms are derived from a specification of the desired behavior, offer hope to address this problem, since there is a combinatorial reduction in complexity in programming in terms of specifications instead of algorithms, and arbitrary desired properties can be expressed and enforced in specifications directly. However, limitations on performance have prevented programming with declarative specifications from becoming a mainstream technique for general-purpose programming. To address the performance bottleneck in deriving an algorithm from a specification, I propose information-gain computation, a framework where an adaptive evaluation strategy is used to efficiently perform a search which derives algorithms that provide information about a query via the most efficient routes. Within this framework, opportunities to compress the search space present themselves, which suggest that information-theoretic bounds on the performance of such a system might be articulated and a system designed to achieve them. In a preliminary empirical study of adaptive evaluation for a simple test program, the evaluation strategy adapts successfully to evaluate a query efficiently.

Stress is bad for our physical and mental health. It has been linked to several leading causes of death, including heart disease and mood disorders, such as depression. Now new research suggests that the actual number of stressful experiences we encounter can have dramatic consequences for the health of our brains. In all, 27 events were identified as being particularly detrimental. These include being expelled from school during adolescence and experiencing unemployment as an adult.

Ray Kurzweil has invented a few things in his time. In his teens, he built a computer that composed classical music, which won him an audience with President Lyndon B. Johnson. In his 20s, he pioneered software that could digitize printed text, and in his 30s he cofounded a synthesizer company with Stevie Wonder. More recently, he's known for popularizing the idea of the singularity--a moment sometime in the future when superintelligent machines transform humanity--and making optimistic predictions about immortality. For now, though, Kurzweil, 69, leads a team of about 35 people at Google whose code helps you write emails.

Your baby's first words are a critical milestone in their development - and parents focus on encouraging them with'baby talk'. But now researchers have shown the importance of sleep when it comes to an infant learning to talk. In particular, sleep helps the important process of associating meanings to words - and not just perceive them as random noise. They found that babies as young as six to eight months old were capable of the association - which until now was thought to happen later on. And this occurred as a direct result of them being put down for a midday nap, the researchers say.

The eyes really do have it when people look for love, new research reveals. A study found that men and women rate a person's eyes more important than other facial features when seeking for a potential partner. Having attractive hair and lips is also an important factor in the beauty stakes. The least important facial feature seems to be someone's nose, researchers found. On average, people found eyes the most attractive, then hair, then the whole configuration then lips and finally nose.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

Hi, I wish I could help in such way. I myself using the Link Model to observe and study repeated events . My sampling study was "Random or Causality" for drawing winning lottery numbers. The term Regression is some how a slow process of continuity of events, regarding THE MODEL THAT is used. I only observed activities of all Celestial Bodies that caused things to happen the way they happened.

DUBLIN--(BUSINESS WIRE)--The "Artificial Intelligence in Healthcare Market by Offering - Global Opportunity Analysis and Industry Forecast, 2017-2023" report has been added to Research and Markets' offering. The global AI market was valued at $1,441 million in 2016, and is estimated to reach at $22,790 million by 2023, registering a CAGR of 48.0% from 2017 to 2023. The market growth is driven by rise in adoption rate of AI systems and delete technological advancements in the AI field. In addition, the ability of these systems to improve patient outcomes, increase in adoption of precision medicine, and increase in need for coordination between healthcare workforce & patients are expected to fuel the market growth. However, lack of standard regulations and guidelines and reluctance among healthcare professionals to adopt AI-based technologies are expected to hinder the market growth. The deep learning segment accounted for the highest share in 2016 and is expected to dominate the market from 2017 to 2023, owing to increase in use of signal reduction, data mining, and image recognition.

If the next era of human progress is built using AI, who gets to engineer it? Who will have the coding skills to use the software for creating AI products, or even more importantly, the skills to write that software? In an attempt to make the answer to those questions "anyone who wants to," Andrew Ng is releasing a new set of courses teaching deep learning on Coursera, the online learning platform he co-founded in 2012. Coursera was originally set up to offer an online class in machine learning; deep learning is a variety of that, involving exceptionally large datasets. The original machine learning course attracted more than 2 million students, Ng tells MIT Tech Review.