This article reviews machine learning methods for bioinformatics. It presents modelling methods, such as supervised classification, clustering and probabilistic graphical models for knowledge discovery, as well as deterministic and stochastic heuristics for optimization. Applications in genomics, proteomics, systems biology, evolution and text mining are also shown.

/pdf/machine-learning-in-bioinformatics-5gfm1x1ssk.pdf

Machine learning in bioinformatics

Large amounts of data are increasingly accumulated in the energy sector with the continuous application of sensors, wireless transmission, network communication, and cloud computing technologies. To fulfill the potential of energy big data and obtain insights to achieve smart energy management, we present a comprehensive study of big data driven smart energy management. We first discuss the sources and characteristics of energy big data. Also, a process model of big data driven smart energy management is proposed. Then taking smart grid as the research background, we provide a systematic review of big data analytics for smart energy management. It is discussed from four major aspects, namely power generation side management, microgrid and renewable energy management, asset management and collaborative operation, as well as demand side management (DSM). Afterwards, the industrial development of big data-driven smart energy management is analyzed and discussed. Finally, we point out the challenges of big data-driven smart energy management in IT infrastructure, data collection and governance, data integration and sharing, processing and analysis, security and privacy, and professionals.

Big data driven smart energy management: From big data to big insights

In the past two years, several applications of generating three-dimensional 3D buildings from OpenStreetMap OSM have been made available, for instance, OSM-3D, OSM2World, OSM Building, etc In these projects, 3D buildings are reconstructed using the buildings’ footprints and information about their attributes, which are documented as tags in OSM Therefore, the quality of 3D buildings relies strongly on the quality of the building footprints data in OSM This article is dedicated to a quality assessment of building footprints data in OSM for the German city of Munich, which is one of the most developed cities in OSM The data are evaluated in terms of completeness, semantic accuracy, position accuracy, and shape accuracy by using building footprints in ATKIS German Authority Topographic–Cartographic Information System as reference data The process contains three steps: finding correspondence between OSM and ATKIS data, calculating parameters of the four quality criteria, and statistical analysis The results show that OSM footprint data in Munich have a high completeness and semantic accuracy There is an offset of about four meters on average in terms of position accuracy With respect to shape, OSM building footprints have a high similarity to those in ATKIS data However, some architectural details are missing; hence, the OSM footprints can be regarded as a simplified version of those in ATKIS data

Quality assessment for building footprints data on OpenStreetMap

Statistical Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1, 124, 254, 297 History Corner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .20, 179 Teacher’s Corner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26, 173, 263, 335 General . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40, 147, 211, 366 Statistical Computing and Graphics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 Statistical Computing and Software Reviews . . . . . . . . . . . . . . . . . . . . . . . . 75, 187 Reviews of Books and Teaching Materials . . . . . . . . . . . . . . . . . 92, 189, 281, 401 Brief Reviews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100, 195, 292, 404 Letters to the Editor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102, 197, 294, 406 Special Section: Statistical Training and Curricular Revision . . . . . . . . . . . . . 105 Errata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200 Special Section: Opportunities and Challenges for the Discipline . . . . . . . . . 201 Software Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

The American statistician

User-generated content (UGC) platforms on the Internet have experienced a steep increase in data contributions in recent years. The ubiquitous usage of location-enabled devices, such as smartphones, allows contributors to share their geographic information on a number of selected online portals. The collected information is oftentimes referred to as volunteered geographic information (VGI). One of the most utilized, analyzed and cited VGI-platforms, with an increasing popularity over the past few years, is OpenStreetMap (OSM), whose main goal it is to create a freely available geographic database of the world. This paper presents a comprehensive overview of the latest developments in VGI research, focusing on its collaboratively collected geodata and corresponding contributor patterns. Additionally, trends in the realm of OSM research are discussed, highlighting which aspects need to be investigated more closely in the near future.

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Recent Developments and Future Trends in Volunteered Geographic Information Research: The Case of OpenStreetMap

This paper investigates the problems that arise when application requirements dictate that autonomous spatial databases be integrated into a federated one. The paper focuses on the most critical issues raised by the integration of databases of different scales. A short presentation of approaches to interoperability and of the main steps composing the integration process is given first. Next, a general format is proposed for precisely defining correspondences between objects of two databases. The format can deal with a wide range of discrepancies in GIS data. Last, a solution is presented for aggregation conflicts which arise when one object of one database corresponds to a set of objects in the other database, a very frequent case when the databases are of different scales. The method is applied to excerpts of real cartographic databases.

On Spatial Database Integration

More and more mobile objects are now equipped with sensors allowing real time monitoring of their movements. Nowadays, the data produced by these sensors can be stored in spatio-temporal databases. The main goal of this article is to perform a data mining on a huge quantity of mobile object’s positions moving in an open space in order to deduce its behaviour. New tools must be defined to ease the detection of outliers. First of all, a zone graph is set up in order to define itineraries. Then, trajectories of mobile objects following the same itinerary are extracted from the spatio-temporal database and clustered. A statistical analysis on this set of trajectories lead to spatio-temporal patterns such as the main route and spatio-temporal channel followed by most of trajectories of the set. Using these patterns, unusual situations can be detected. Furthermore, a mobile object’s behaviour can be defined by comparing its positions with these spatio-temporal patterns. In this article, this technique is applied to ships’ movements in an open maritime area. Unusual behaviours such as being ahead of schedule or delayed or veering to the left or to the right of the main route are detected. A case study illustrates these processes based on ships’ positions recorded during two years around the Brest area. This method can be extended to almost all kinds of mobile objects (pedestrians, aircrafts, hurricanes, ...) moving in an open area.

/pdf/spatio-temporal-trajectory-analysis-of-mobile-objects-mq9peih604.pdf

Spatio-temporal trajectory analysis of mobile objects following the same itinerary

The overlay process is currently one of the main computational solutions used to integrate several data layers from different sources. Unfortunately, it is problematic when trying to overlay many layers. This leads to several geometric problems such as the management of sliver polygons. This paper proposes a new merging process to complement the vector overlay for data integration of several layers. This process, based on measures derived from the Frechet distance, matches common points (either lines or polygons). It also merges an ordered set of pairs of matching points (vertices) into a single geometry.

/pdf/a-new-merging-process-for-data-integration-based-on-the-4yyy9dwlq0.pdf

A New Merging Process for Data Integration Based on the Discrete Fréchet Distance

Building multiple representations is one of the key problems in GIS. To tackle this problem, we have chosen to connect geographic data from mono-scale representations to build a multi-scale database with scale-transition relationships. These scale-transition relationships connect two sets of elements (classes, types or objects) representing the same phenomenon of the real world and carry the sequence of multi-scale operations to navigate from one representation to another. From this concept, a process has been defined to build multi-scale databases, in three steps. The first step is dedicated to the declaration of correspondences and conflicts between input schemata by the means of scale-transition relationships. In the second step, conflicts are resolved and schemata are merged. Finally, the third step corresponds to data matching, with the help of geometric, topologic and semantic information. Scale-transition relationships between objects are created during this last step. To validate the process, a multi-scale database has been produced from two existing mono-scale sets of road network data. The first results of this kernel are satisfactory.

Building a Multi-Scale Database with Scale-Transition Relationships

Spatial distances are the main tools used for data matching and control quality. This paper describes new measures adapted to sinuous lines to compute the maximal and average discrepancy: Discrete Frechet distance and Discrete Average Frechet distance. Afterwards, a global process is defined to automatically handle two sets of lines. The usefulness of these distances is tested, with a comparison of coastlines. The validation is done with the computation of three sets of coastlines, obtained respectively from SPOT 5 orthophotographs and GPS points. Finally, an extension to Digital Elevation Model is presented.

/pdf/coastline-matching-process-based-on-the-discrete-frechet-44j9opue9c.pdf

Coastline Matching Process Based on the Discrete Fréchet Distance

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