On the role of pre and post-processing in environmental data mining

The quality of discovered knowledge is highly depending on data quality. Unfortunately real data use to contain noise, uncertainty, errors, redundancies or even irrelevant information. The more complex is the reality to be analyzed, the higher the risk of getting low quality data. Knowledge Discovery from Databases (KDD) offers a global framework to prepare data in the right form to perform correct analyses. On the other hand, the quality of decisions taken upon KDD results, depend not only on the quality of the results themselves, but on the capacity of the system to communicate those results in an understandable form. Environmental systems are particularly complex and environmental users particularly require clarity in their results. In this paper some details about how this can be achieved are provided. The role of the pre and post processing in the whole process of Knowledge Discovery in environmental systems is discussed

The 1990 progress report and future plans

This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers

Data mining as a tool for environmental scientists

Over recent years a huge library of data mining algorithms has been developed to tackle a variety of problems in fields such as medical imaging and network traffic analysis. Many of these techniques are far more flexible than more classical modelling approaches and could be usefully applied to data-rich environmental problems. Certain techniques such as Artificial Neural Networks, Clustering, Case-Based Reasoning and more recently Bayesian Decision Networks have found application in environmental modelling while other methods, for example classification and association rule extraction, have not yet been taken up on any wide scale. We propose that these and other data mining techniques could be usefully applied to difficult problems in the field. This paper introduces several data mining concepts and briefly discusses their application to environmental modelling, where data may be sparse, incomplete, or heterogenous

Conceptual Based Hidden Data Analytics and Reduction Method for System Interface Enhancement Through Handheld devices

With the increasing demand placed on online systems by users, many organizations and companies are seeking to enhance their online interfaces to facilitate the search process on their hidden databases. Usually, users issue queries to a hidden database by using the search template provided by the system. In this thesis, a new approach based mainly on hidden database reduction preserving functional dependencies is developed for enhancing the online system interface through a small screen device. The developed approach is applied to online market systems like eBay. Offline hidden data analysis is used to discover attributes and their domains and different functional dependencies. In this thesis, a comparative study between several methods for mining functional dependencies shows the advantage of conceptual methods for data reduction. In addition, by using online consecutive reductions on search results, we adopted a method of displaying results in order of decreasing relevance. The validation of the proposed designed and developed methods prove their generality and suitability for system interfacing through continuous data reductions.NPRP-07-794-1-145 grant from the Qatar National Research Fund (a member of Qatar foundation

Development of novel orthogonal genetic circuits, based on extracytoplasmic function (ECF) σ factors

The synthetic biology field aims to apply the engineering 'design-build-test-learn' cycle for the implementation of synthetic genetic circuits modifying the behavior of biological systems. In order to reach this goal, synthetic biology projects use a set of fully characterized biological parts that subsequently are assembled into complex synthetic circuits following a rational, model-driven design. However, even though the bottom-up design approach represents an optimal starting point to assay the behavior of the synthetic circuits under defined conditions, the rational design of such circuits is often restricted by the limited number of available DNA building blocks. These usually consist only of a handful of transcriptional regulators that additionally are often borrowed from natural biological systems. This, in turn, can lead to cross-reactions between the synthetic circuit and the host cell and eventually to loss of the original circuit function. Thus, one of the challenges in synthetic biology is to design synthetic circuits that perform the designated functions with minor cross-reactions (orthogonality). To overcome the restrictions of the widely used transcriptional regulators, this project aims to apply extracytoplasmic function (ECF) σ factors in the design novel orthogonal synthetic circuits. ECFs are the smallest and simplest alternative σ factors that recognize highly specific promoters. ECFs represent one of the most important mechanisms of signal transduction in bacteria, indeed, their activity is often controlled by anti-σ factors. Even though it was shown that the overexpression of heterologous anti-σ factors can generate an adverse effect on cell growth, they represent an attractive solution to control ECF activity. Finally, to date, we know thousands of ECF σ factors, widespread among different bacterial phyla, that are identifiable together with the cognate promoters and anti-σ factors, using bioinformatic approaches. All the above-mentioned features make ECF σ factors optimal candidates as core orthogonal regulators for the design of novel synthetic circuits. In this project, in order to establish ECF σ factors as standard building blocks in the synthetic biology field, we first established a high throughput experimental setup. This relies on microplate reader experiments performed using a highly sensitive luminescent reporter system. Luminescent reporters have a superior signal-to-noise ratio when compared to fluorescent reporters since they do not suffer from the high auto-fluorescence background of the bacterial cell. However, they also have a drawback represented by the constant light emission that can generate undesired cross-talk between neighboring wells on a microplate. To overcome this limitation, we developed a computational algorithm that corrects for luminescence bleed-through and estimates the “true” luminescence activity for each well of a microplate. We show that the correcting algorithm preserves low-level signals close to the background and that it is universally applicable to different experimental conditions. In order to simplify the assembly of large ECF-based synthetic circuits, we designed an ECF toolbox in E. coli. The toolbox allows for the combinatorial assembly of circuits into expression vectors, using a library of reusable genetic parts. Moreover, it also offers the possibility of integrating the newly generated synthetic circuits into four different phage attachment (att) sites present in the genome of E. coli. This allows for a flawless transition between plasmid-encoded and chromosomally integrated genetic circuits, expanding the possible genetic configurations of a given synthetic construct. Moreover, our results demonstrate that the four att sites are orthogonal in terms of the gene expression levels of the synthetic circuits. With the purpose of rationally design ECF-based synthetic circuits and taking advantage of the ECF toolbox, we characterized the dynamic behavior of a set of 15 ECF σ factors, their cognate promoters, and relative anti-σs. Overall, we found that ECFs are non-toxic and functional and that they display different binding affinities for the cognate target promoters. Moreover, our results show that it is possible to optimize the output dynamic range of the ECF-based switches by changing the copy number of the ECFs and target promoters, thus, tuning the input/output signal ratio. Next, by combining up to three ECF-switches, we generated a set of “genetic-timer circuits”, the first synthetic circuits harboring more than one ECF. ECF-based timer circuits sequentially activate a series of target genes with increasing time delays, moreover, the behavior of the circuits can be predicted by a set of mathematical models. In order to improve the dynamic response of the ECF-based constructs, we introduced anti-σ factors in our synthetic circuits. By doing so we first confirmed that anti-σ factors can exert an adverse effect on the growth of E. coli, thus we explored possible solutions. Our results demonstrate that anti-σ factors toxicity can be partially alleviated by generating truncated, soluble variants of the anti-σ factors and, eventually, completely abolished via chromosomal integration of the anti-σ factor-based circuits. Finally, after demonstrating that anti-σ factors can be used to generate a tunable time delay among ECF expression and target promoter activation, we designed ECF/AS-suicide circuits. Such circuits allow for the time-delayed cell-death of E. coli and will serve as a prototype for the further development of ECF/AS-based lysis circuits

Web Data Extraction, Applications and Techniques: A Survey

Web Data Extraction is an important problem that has been studied by means of different scientific tools and in a broad range of applications. Many approaches to extracting data from the Web have been designed to solve specific problems and operate in ad-hoc domains. Other approaches, instead, heavily reuse techniques and algorithms developed in the field of Information Extraction. This survey aims at providing a structured and comprehensive overview of the literature in the field of Web Data Extraction. We provided a simple classification framework in which existing Web Data Extraction applications are grouped into two main classes, namely applications at the Enterprise level and at the Social Web level. At the Enterprise level, Web Data Extraction techniques emerge as a key tool to perform data analysis in Business and Competitive Intelligence systems as well as for business process re-engineering. At the Social Web level, Web Data Extraction techniques allow to gather a large amount of structured data continuously generated and disseminated by Web 2.0, Social Media and Online Social Network users and this offers unprecedented opportunities to analyze human behavior at a very large scale. We discuss also the potential of cross-fertilization, i.e., on the possibility of re-using Web Data Extraction techniques originally designed to work in a given domain, in other domains.Comment: Knowledge-based System