摘要：The fast development of Web technologies has introduced a world of big data. How efficiently and effectively to retrieve the information from the ocean of data that the users really want is an important topic. Recommendation systems have become a popular approach to personalized information retrieval. On the other hand, social media have quickly entered into your life. The information from social networks can be an effective indicator for recommender systems. In this paper we present a recommendation mechanism which calculates similarity among users and users' trustability and analyzes information collected from social networks. To validate our method an information system for tourist attractions built on this recommender system has been presented. We further evaluate our system by experiments. The results show our method is feasible and effective.

摘要：Despite the same purpose, each satellite product has different value because of its inescapable uncertainty. Also the satellite products have been calculated for a long time, and the kinds of the products are various and enormous. So the efforts for reducing the uncertainty and dealing with enormous data will be necessary. In this paper, we create an ensemble Sea Surface Temperature(SST) using MODIS Aqua, MODIS Terra and COMS(Communication Ocean & Meteorological Satellite). We used Bayesian Model Averaging(BMA) as ensemble method. The principle of the BMA is synthesizing the conditional probability density function(PDF) using posterior probability as weight. The posterior probability is estimated using EM algorithm. The BMA PDF is obtained by weighted average. As the result, the ensemble SST showed the lowest RMSE and MAE, which proves the applicability of BMA for satellite data ensemble. As future work, parallel processing techniques using Hadoop framework will be adopted for more efficient computation of very big satellite data.

摘要：In the era of big data it is increasingly difficult for an analyst to extract meaningful knowledge from a sea of information. We present TweetXplorer, a system for analysts with little information about an event to gain knowledge through the use of effective visualization techniques. Using tweets collected during Hurricane Sandy as an example, we will lead the reader through a workflow that exhibits the functionality of the system.

摘要：Marine biotechnology is the industrial, medical or environmental application of biological resources from the sea. Since the marine environment is the most biologically and chemically diverse habitat on the planet, marine biotechnology has, in recent years delivered a growing number of major therapeutic products, industrial and environmental applications and analytical tools. These range from the use of a snail toxin to develop a pain control drug, metabolites from a sea squirt to develop an anti-cancer therapeutic, and marine enzymes to remove bacterial biofilms. In addition, well known and broadly used analytical techniques are derived from marine molecules or enzymes, including green fluorescence protein gene tagging methods and heat resistant polymerases used in the polymerase chain reaction. Advances in bacterial identification, metabolic profiling and physical handling of cells are being revolutionised by techniques such as mass spectrometric analysis of bacterial proteins. Advances in instrumentation and a combination of these physical advances with progress in proteomics and bioinformatics are accelerating our ability to harness biology for commercial gain. Single cell Raman spectroscopy and microfluidics are two emerging techniques which are also discussed elsewhere in this issue. In this review, we provide a brief survey and update of the most powerful and rapidly growing analytical techniques as used in marine biotechnology, together with some promising examples of less well known earlier stage methods which may make a bigger impact in the future.

摘要：For decades, scientists bemoaned the scarcity of observational data to analyze and against which to test their models. Exponential growth in data volumes from ever-cheaper environmental sensors has provided scientists with the answer to their prayers: "big data". Now, scientists face a new challenge: with terabytes, petabytes or exabytes of data at hand, stored in thousands of heterogeneous datasets, how can scientists find the datasets most relevant to their research interests? If they cannot find the data, then they may as well never have collected it; that data is lost to them. Our research addresses this challenge, using an existing scientific archive as our test-bed. We approach this problem in a new way: by adapting Information Retrieval techniques, developed for searching text documents, into the world of (primarily numeric) scientific data. We propose an approach that uses a blend of automated and "semi-curated" methods to extract metadata from large archives of scientific data. We then perform searches over the extracted metadata, returning results ranked by similarity to the query terms. We briefly describe an implementation performed at an ocean observatory to validate the proposed approach. We propose performance and scalability research to explore how continued archive growth will affect our goal of interactive response, no matter the scale.

摘要：This perspectives paper uses the seminal article of Goodchild (1992) as a lens through which to review and reflect upon several longstanding issues that have influenced the field of geographic information science in the past and will continue to be important at least into the next decade. Under the category of theory, the continuing issue of 'tool versus science' now has implications for the defining of geographic information systems (GISs) as a profession. In turn, a brief perspective is offered on how GIS has contributed to 'methodological versus substantive' questions in science, leading to the understanding of how the Earth works versus how the Earth should look. Both understandings of the Earth are particularly germane to the emergence of tools and applications such as marine and coastal GIS, virtual globes, spatial cyber infrastructure, and the ethics of GIS. And in the realm of marine and coastal GIS, the example of multidimensional data structuring and scaling is used to highlight an underlying lesson of Goodchild (1992) in that theory and application are in no way mutually exclusive, and it may often be application that advances theory, rather than vice versa. Indeed, it may be this reversal that is ushering in a 'post-GISystems' world, where GIS is subsumed into a broader framework known simply as 'the web,' divorced from the desktop, but providing a new paradigm for GIS (aligned with the 'fourth paradigm' of Hey et al. 2009). As so much data and information will be collected spatially in a way not possible before (e. g., the 'big data' of global observational science), GIS will need to be both system and science to support the turn toward more place-based research across increasing scientific domains. GIS is needed also by society at large to guide the understanding of the longstanding fundamentals of geolocation, scale, proximity, distance decay, the neighborhood, the region, the territory, and more.

摘要：Some science domains have the advantage that the bulk of the data comes from a single source instrument, such as a telescope or particle collider. More commonly, big data implies a big variety of data sources. For example, the Center for Coastal Margin Observation and Prediction (CMOP) has multiple kinds of sensors (salinity, temperature, pH, dissolved oxygen, chlorophyll A & B) on diverse platforms (fixed station, buoy, ship, underwater robot) coming in at different rates over various spatial scales and provided at several quality levels (raw, preliminary, curated). In addition, there are physical samples analyzed in the lab for biochemical and genetic properties, and simulation models for estuaries and near-ocean fluid dynamics and biogeochemical processes. Few people know the entire range of data holdings, much less their structures and how to access them. We present a variety of approaches CMOP has followed to help operational, science and resource managers locate, view and analyze data, including the Data Explorer, Data Near Here, and topical "watch pages." From these examples, and user experiences with them, we draw lessons about supporting users of collaborative "science observatories" and remaining challenges.

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摘要：Our understanding of the natural history of breast cancer has evolved alongside technologies to study its genomic, transcriptomic, proteomic, and metabolomics landscapes. These technologies have helped decipher multiple molecular pathways dysregulated in breast cancer. First-generation 'omics analyses considered each of these dimensions individually, but it is becoming increasingly clear that more holistic, integrative approaches are required to fully understand complex biological systems. The 'omics represent an exciting era of discovery in breast cancer research, although important issues need to be addressed to realize the clinical utility of these data through precision cancer care. How can the data be applied to predict response to molecular-targeted therapies? When should treatment decisions be based on tumor genetics rather than histology? And with the sudden explosion of "big data" from large 'omics consortia and new precision clinical trials, how do we now negotiate evidence-based pathways to clinical translation through this apparent sea of opportunity? The aim of this review is to provide a broad overview of 'omics technologies used in breast cancer research today, the current state-of-play in terms of applying this new knowledge in the clinic, and the practical and ethical issues that will be central to the public discussion on the future of precision cancer care.

摘要：As social media is opening up such as Twitter and Sina Weibo,(1) large volumes of short texts are flooding on the Web. The ocean of short texts dilutes the limited core semantics of event in cyberspace by redundancy, noises and irrelevant content on the web, which make it difficult to discover the core semantics of event. The major challenges include how to efficiently learn the semantic association distribution by small-scale association relations and how to maximize the coverage of the semantic association distribution by the minimum number of redundancy-free short texts. To solve the above issues, we explore a Markov random field based method for discovering the core semantics of event. This method makes semantics collaborative computation for learning association relation distribution and makes information gradient computation for discovering k redundancy-free texts as the core semantics of event. We evaluate our method by comparing with two state-of-the-art methods on the TAC dataset and the microblog dataset. The results show our method outperforms other methods in extracting core semantics accurately and efficiently. The proposed method can be applied to short text automatic generation, event discovery and summarization for big data analysis. (C) 2015 Elsevier B.V. All rights reserved.