Slicing big data - Twitter, gambling and time sensitive information

Big Data presents many challenges related to volume, whether one is interested in studying past datasets or, even more problematically, attempting to work with live streams of data. The most obvious challenge, in a ‘noisy’ environment such as contemporary social media, is to collect the pertinent information; be that information for a specific study, tweets which can inform emergency services or other responders to an ongoing crisis, or give an advantage to those involved in prediction markets. Often, such a process is iterative, with keywords and hashtags changing with the passage of time, and both collection and analytic methodologies need to be continually adapted to respond to this changing information. While many of the data sets collected and analyzed are preformed, that is they are built around a particular keyword, hashtag, or set of authors, they still contain a large volume of information, much of which is unnecessary for the current purpose and/or potentially useful for future projects. Accordingly, this panel considers methods for separating and combining data to optimize big data research and report findings to stakeholders. The first paper considers possible coding mechanisms for incoming tweets during a crisis, taking a large stream of incoming tweets and selecting which of those need to be immediately placed in front of responders, for manual filtering and possible action. The paper suggests two solutions for this, content analysis and user profiling. In the former case, aspects of the tweet are assigned a score to assess its likely relationship to the topic at hand, and the urgency of the information, whilst the latter attempts to identify those users who are either serving as amplifiers of information or are known as an authoritative source. Through these techniques, the information contained in a large dataset could be filtered down to match the expected capacity of emergency responders, and knowledge as to the core keywords or hashtags relating to the current event is constantly refined for future data collection. The second paper is also concerned with identifying significant tweets, but in this case tweets relevant to particular prediction market; tennis betting. As increasing numbers of professional sports men and women create Twitter accounts to communicate with their fans, information is being shared regarding injuries, form and emotions which have the potential to impact on future results. As has already been demonstrated with leading US sports, such information is extremely valuable. Tennis, as with American Football (NFL) and Baseball (MLB) has paid subscription services which manually filter incoming news sources, including tweets, for information valuable to gamblers, gambling operators, and fantasy sports players. However, whilst such services are still niche operations, much of the value of information is lost by the time it reaches one of these services. The paper thus considers how information could be filtered from twitter user lists and hash tag or keyword monitoring, assessing the value of the source, information, and the prediction markets to which it may relate. The third paper examines methods for collecting Twitter data and following changes in an ongoing, dynamic social movement, such as the Occupy Wall Street movement. It involves the development of technical infrastructure to collect and make the tweets available for exploration and analysis. A strategy to respond to changes in the social movement is also required or the resulting tweets will only reflect the discussions and strategies the movement used at the time the keyword list is created — in a way, keyword creation is part strategy and part art. In this paper we describe strategies for the creation of a social media archive, specifically tweets related to the Occupy Wall Street movement, and methods for continuing to adapt data collection strategies as the movement’s presence in Twitter changes over time. We also discuss the opportunities and methods to extract data smaller slices of data from an archive of social media data to support a multitude of research projects in multiple fields of study. The common theme amongst these papers is that of constructing a data set, filtering it for a specific purpose, and then using the resulting information to aid in future data collection. The intention is that through the papers presented, and subsequent discussion, the panel will inform the wider research community not only on the objectives and limitations of data collection, live analytics, and filtering, but also on current and in-development methodologies that could be adopted by those working with such datasets, and how such approaches could be customized depending on the project stakeholders.

an intuitive formal proof for deadline driven scheduler

United Nations University, Int. Inst. for Softw. Technol., China; Vietnam National University, Hanoi, Vietnam; Vietnam Academy of Science and Technology, Vietnam

Evaluation of the matabolic response of osteosarcoma cells to conventional and new anticancer drugs by NMR metabolomics

The main scope of this work was to evaluate the metabolic effects of anticancer agents (three conventional and one new) in osteosarcoma (OS) cells and osteoblasts, by measuring alterations in the metabolic profile of cells by nuclear magnetic resonance (NMR) spectroscopy metabolomics. Chapter 1 gives a theoretical framework of this work, beginning with the main metabolic characteristics that globally describe cancer as well as the families and mechanisms of action of drugs used in chemotherapy. The drugs used nowadays to treat OS are also presented, together with the Palladium(II) complex with spermine, Pd2Spm, potentially active against cancer. Then, the global strategy for cell metabolomics is explained and the state of the art of metabolomic studies that analyze the effect of anticancer agents in cells is presented. In Chapter 2, the fundamentals of the analytical techniques used in this work, namely for biological assays, NMR spectroscopy and multivariate and statistical analysis of the results are described. A detailed description of the experimental procedures adopted throughout this work is given in Chapter 3. The biological and analytical reproducibility of the metabolic profile of MG-63 cells by high resolution magic angle spinning (HRMAS) NMR is evaluated in Chapter 4. The metabolic impact of several factors (cellular integrity, spinning rate, temperature, time and acquisition parameters) on the 1H HRMAS NMR spectral profile and quality is analysed, enabling the definition of the best acquisition parameters for further experiments. The metabolic consequences of increasing number of passages in MG-63 cells as well as the duration of storage are also investigated. Chapter 5 describes the metabolic impact of drugs conventionally used in OS chemotherapy, through NMR metabolomics studies of lysed cells and aqueous extracts analysis. The results show that MG-63 cells treated with cisplatin (cDDP) undergo a strong up-regulation of lipid contents, alterations in phospholipid constituents (choline compounds) and biomarkers of DNA degradation, all associated with cell death by apoptosis. Cells exposed to doxorubicin (DOX) or methotrexate (MTX) showed much slighter metabolic changes, without any relevant alteration in lipid contents. However, metabolic changes associated with altered Krebs cycle, oxidative stress and nucleotides metabolism were detected and were tentatively interpreted at the light of the known mechanisms of action of these drugs. The metabolic impact of the exposure of MG-63 cells and osteoblasts to cDDP and the Pd2Spm complex is described in Chapter 6. Results show that, despite the ability of the two agents to bind DNA, the metabolic consequences that arise from exposure to them are distinct, namely in what concerns to variation in lipid contents (absent for Pd2Spm). Apoptosis detection assays showed that, differently from what was seen for MG-63 cells treated with cDDP, the decreased number of living cells upon exposure to Pd2Spm was not due to cell death by apoptosis or necrosis. Moreover, the latter agent induces more marked alterations in osteoblasts than in cancer cells, while the opposite seemed to occur upon cDDP exposure. Nevertheless, the results from MG-63 cells exposure to combination regimens with cDDP- or Pd2Spm-based cocktails, described in Chapter 7, revealed that, in combination, the two agents induce similar metabolic responses, arising from synergy mechanisms between the tested drugs. Finally, the main conclusions of this thesis are summarized in Chapter 8, and future perspectives in the light of this work are presented.

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Identification of synchronous machine parameters using load rejection test data

This work shows a computational methodology for the determination of synchronous machines parameters using load rejection test data. By machine modeling one can obtain the quadrature parameters through a load rejection under an arbitrary reference, reducing the present difficulties. The proposed method is applied to a real machine.