The document describes a data fusion system that automatically fuses imperfect geospatial data from multiple sources to produce a single, higher quality dataset. The system has three main components - preprocessing input data, filtering/fusing the data, and validating the merged output. It uses a modular architecture and processes data through conversion, analysis, relationship detection, attribute transfer, and quality assessment steps. The system provides both command line and graphical user interfaces and aims to improve on existing data through automated harmonization.
FPGA configuration of an alloyed correlated branch predictor used with RISC p...IJECEIAES
Instructions pipelining is one of the most outstanding techniques used in improving processor speed; nonetheless, these pipelined stages are constantly facing stalls that caused by nested conditional branches. During the execution of nested conditional branches, the behavior of the running branch depends on the history information of the previous ones; therefore, these branches have the greatest effect in reducing the prediction accuracy of a branch predictor among conditional branches. The purpose of this research is to reduce the stall cycles caused by correlated branches misprediction by introducing a hardware model of a branch predictor that combines both local and global prediction techniques. This predictor integrates the prediction characteristics of the alloyed predictor with those of the correlated predictor. the predictor design which implemented in VHDL (Very high-speed IC hardware description language) was inserted in previously designed MIPS (microprocessor without interlocked pipelined stages) processor and its prediction accuracy was confirmed by executing a program using the selection sort algorithm to sort 100 input numbers of different combinations ascendingly.
MetOp Satellites Data Processing for Air Pollution Monitoring in Morocco IJECEIAES
This paper presents a data processing system based on an architecture comprised of multiple stacked layers of computational processes that transforms Raw Binary Pollution Data coming directly from Two EUMETSAT MetOp satellites to our servers, into ready to interpret and visualise continuous data stream in near real time using techniques varying from task automation, data preprocessing and data analysis to machine learning using feedforward artificial neural networks. The proposed system handles the acquisition, cleaning, processing, normalizing, and predicting of Pollution Data in our area of interest of Morocco.
Lecture Notes by Mustafa Jarrar at Birzeit University, Palestine.
See the course webpage at: http://jarrar-courses.blogspot.com/2014/01/data-schema-integration.html and http://www.jarrar.info
you may also watch this lecture at: http://www.youtube.com/watch?v=VJtF_7ptln4
The lecture covers:
- Challenges of Data Schema Integration
- Framework for Schema Integration
- Schema Transformation
- Reverse Engineering
FPGA configuration of an alloyed correlated branch predictor used with RISC p...IJECEIAES
Instructions pipelining is one of the most outstanding techniques used in improving processor speed; nonetheless, these pipelined stages are constantly facing stalls that caused by nested conditional branches. During the execution of nested conditional branches, the behavior of the running branch depends on the history information of the previous ones; therefore, these branches have the greatest effect in reducing the prediction accuracy of a branch predictor among conditional branches. The purpose of this research is to reduce the stall cycles caused by correlated branches misprediction by introducing a hardware model of a branch predictor that combines both local and global prediction techniques. This predictor integrates the prediction characteristics of the alloyed predictor with those of the correlated predictor. the predictor design which implemented in VHDL (Very high-speed IC hardware description language) was inserted in previously designed MIPS (microprocessor without interlocked pipelined stages) processor and its prediction accuracy was confirmed by executing a program using the selection sort algorithm to sort 100 input numbers of different combinations ascendingly.
MetOp Satellites Data Processing for Air Pollution Monitoring in Morocco IJECEIAES
This paper presents a data processing system based on an architecture comprised of multiple stacked layers of computational processes that transforms Raw Binary Pollution Data coming directly from Two EUMETSAT MetOp satellites to our servers, into ready to interpret and visualise continuous data stream in near real time using techniques varying from task automation, data preprocessing and data analysis to machine learning using feedforward artificial neural networks. The proposed system handles the acquisition, cleaning, processing, normalizing, and predicting of Pollution Data in our area of interest of Morocco.
Lecture Notes by Mustafa Jarrar at Birzeit University, Palestine.
See the course webpage at: http://jarrar-courses.blogspot.com/2014/01/data-schema-integration.html and http://www.jarrar.info
you may also watch this lecture at: http://www.youtube.com/watch?v=VJtF_7ptln4
The lecture covers:
- Challenges of Data Schema Integration
- Framework for Schema Integration
- Schema Transformation
- Reverse Engineering
Project number: 224348
Project acronym: AEGIS
Project title: Open Accessibility Everywhere: Groundwork, Infrastructure, Standards
Starting date: 1 September 2008
Duration: 48 Months
AEGIS is an Integrated Project (IP) within the ICT programme of FP7
Lecture Notes by Mustafa Jarrar at Birzeit University, Palestine.
See the course webpage at: http://jarrar-courses.blogspot.com/2014/01/web-data-management.html
and http://www.jarrar.info
you may also watch this lecture at: http://www.youtube.com/watch?v=rH9mksypcNw
The lecture covers Data Integration and Fusion
Data integration is a perennial challenge facing large-scale data scientists. Bio-ontologies are useful in this endeavour as sources of synonyms and also for rules-based fuzzy integration pipelines.
Improvement of Spatial Data Quality Using the Data ConflationBeniamino Murgante
Improvement of Spatial Data Quality Using the Data Conflation
Silvija Stankute, Hartmut Asche -Geoinformation Research Group, Department of Geography, University of Potsdam
Project number: 224348
Project acronym: AEGIS
Project title: Open Accessibility Everywhere: Groundwork, Infrastructure, Standards
Starting date: 1 September 2008
Duration: 48 Months
AEGIS is an Integrated Project (IP) within the ICT programme of FP7
Lecture Notes by Mustafa Jarrar at Birzeit University, Palestine.
See the course webpage at: http://jarrar-courses.blogspot.com/2014/01/web-data-management.html
and http://www.jarrar.info
you may also watch this lecture at: http://www.youtube.com/watch?v=rH9mksypcNw
The lecture covers Data Integration and Fusion
Data integration is a perennial challenge facing large-scale data scientists. Bio-ontologies are useful in this endeavour as sources of synonyms and also for rules-based fuzzy integration pipelines.
Improvement of Spatial Data Quality Using the Data ConflationBeniamino Murgante
Improvement of Spatial Data Quality Using the Data Conflation
Silvija Stankute, Hartmut Asche -Geoinformation Research Group, Department of Geography, University of Potsdam
Geometrical DCC-Algorithm for Merging Polygonal Geospatial Data - Silvija Sta...Beniamino Murgante
Geometrical DCC-Algorithm for Merging Polygonal Geospatial Data - Silvija Stankute and Hartmut Asche
University of Potsdam Geoinformation Research Germany
Precision Farming (PF) is introduced and history in short is reviewed. Essential activities of GPS locating, soil mapping, GIS dataprocessing and presentation and VRT application are described. Basic principles of PF are shown to be:
• Precision Farming is the management process of within-field variability.
• This management must bring profit or at least reduce the risk of loss
• This management must reduce the impact of farming on environment.
Techniques used in Precision Farming are described. Economics of Precision Farming is discussed. A general cost/benefit analysis and profitability of PF are reviewed. The price of PF adoption facing a farmer is discussed. Methods of process analysis and activity based costing are shown as useful instruments for PF process analysis and model building. PF process is analysed and process graph is developed.
Impact of Packet Inter-arrival Time Features for Online Peer-to-Peer (P2P) Cl...IJECEIAES
Identification of bandwidth-heavy Internet traffic is important for network administrators to throttle high-bandwidth application traffic. Flow features based classification have been previously proposed as promising method to identify Internet traffic based on packet statistical features. The selection of statistical features plays an important role for accurate and timely classification. In this work, we investigate the impact of packet inter-arrival time feature for online P2P classification in terms of accuracy, Kappa statistic and time. Simulations were conducted using available traces from University of Brescia, University of Aalborg and University of Cambridge. Experimental results show that the inclusion of inter-arrival time (IAT) as an online feature increases simulation time and decreases classification accuracy and Kappa statistic.
Equalizing the amount of processing time for each reducer instead of equalizing the amount of data each process in heterogeneous environment. A lightweight strategy to address the data skew problem among the reductions of MapReduce applications. MapReduce has been widely used in various applications, including web indexing, log analysis, data mining, scientific simulations and machine translations. The data skew refers to the imbalance in the amount of data assigned to each task.Using an innovative sampling method which can achieve a highly accurate approximation to the distribution of the intermediate data by sampling only a small fraction during the map processing and to reduce the data in reducer side. Prioritizing the sampling tasks for partitioning decision and splitting of large keys is supported when application semantics permit.Thus providing a reduced data of total ordered output as a result by range partitioner. In the proposed system, the data reduction is by predicting the reduction orders in parallel data processing using feature and instance selection. The accuracy of the data scale and data skew is effectively improved by CHI-ICF data reduction technique. In the existing system normal data distribution is calculated instead here still efficient distribution of data using the feature selection by χ 2 statistics (CHI) and instance selection by Iterative case filter (ICF) is processed.
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The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
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Real world object (streets, roads, homes etc.) acquisition is carried out by various companies or institutions. The consequence is the development of a variety of heterogeneous geo-spatial datasets, which represent the same area of the real world and are different in their geometrical and thematic accuracy. Insufficient geometrical and thematic accuracy leads to the need for a new method of geo-spatial information acquisition. Geoinformation Research Group at the University of Postdam in the previous works [1], [2] and [3] has developed a method, which increases the thematic and geometrical quality of the available spatial data sets. The new method incorporates the data fusion process.
DAFU has three components: 1) preprocessing of input data sets, 2) fusion/filtering of input data sets, 3) post-processing of end data set.
The developed algorithms were implemented in the widely used interpreted, dynamic programming language PERL. PERL is particularly suitable for the processing and/or manipulation of large ASCII data sets (DAFU works basically with ASCII files). The graphical user interface for easy control of the core routines were developed using the Tk library. During the implementation of DAFU, modular construction of the software was particularly important. This allows generic programming and simplifies considerably the subsequent extension. DAFU contains a core with five modules. Three of them (ATKIS-, TeleAtlas- and Navteq-module) cover the input datasets. The objective is a conversion of input data in to the same internal data structure. If the input data sets during the pre-processing step are converted into the same data structure, the next step calls the assignment module. The assignment module relates individual objects of different data sets. This is necessary condition for the fusion module. The fusion module processes merges two different data structures into one.. This merging takes place according to certain rules, which are given by the user over the graphical user interface (GUI). The GUI module controls not only the core of DAFU, but also the periphery. The periphery includes the input and output modules, and the pre-processing and post-processing modules. The input module supplies the data for the core module, which provides data for the output module. Furthermore the GUI module is responsible also for the visualisation of the input data sets and the output data.
The first DAFU component Pre-processing has the following process steps: Analysis of the input data sets, determination of the data quality and data preparation. The subject of this process step is the analysis of the geo-spatial input information. Here the vector model of every input data set is analysed. In the first pre-processing step each input data set must be converted in a unified coordinate system. This is important for the future geo-data merge. The next step is the transfer of the input data into the same data format. In the third analysis step the uniqueness and completeness of spatial input data sets must be verified. All the data sets used in DAFU systems are vector data. Any spatial object contains geometric and semantic information. Moreover, quality measures (or possibly a quality measure) will be computed. This defines the quality of the available input data sets. To be able to define the quality measure, two characteristics of each input data set have to be examined. The first characteristic is the degree of topological correctness. The second characteristic is the measurement of the thematic completeness. To merge the geo-data with each other, so that they are correct from a topological point of view, it is necessary to carry out geometrical correction of the geo-data. This includes removing duplicate geometries. The result of pre-processing component is output-pre-pro-data sets. These data will be used as input data for the fusion/filtering component.
After the successful analysis and preparation of the input data sets, the input data sets are merged. The DAFU system executes the algorithms based on direct comparison of coordinates. For the fusion of the vector data with different geometrical types, separate algorithms were developed. The requirements for the successful realisation of the subroutines are alike for all geometrical types. The input data sets must have the same coordinate system and the same data format. The next important requirement is redundancy-free input data sets. This means that each object of the space may be represented only with one geometry. All algorithms are based on the direct comparison of the coordinates. The relationship between objects in the various input data sets and objects in the real world are determined by using pairs of coordinates. Using pairs of coordinates are determined relationships between the objects that are present in the various input data sets and represent the same object in the real world. By creating a relation between two objects, the transfer of attributes (thematic information) is ensured. The user-defined set of attributes ensures that thematic information is transmitted over an object from two or more input data sets. This transmission (or cross-referencing) means the extension of the attribute table and generation of new geometrical features. In one implementation of DAFU only one input data set with the other input data set can be extended. The user decides which data set will be extended. The result of fusion/filtering component is the output-merge-data set.
In the post-processing component the output-merge-data set must be verified. The quality of fusion process is calculated and evaluated. This is followed by manual correction of the possible errors, which are usually below five percent for line-like objects and less than 10-15 percent for polygonal objects. The transfer of geo-data in to different coordinate systems follows after manual correction of errors. The next and final step of the post-processing component is to convert merged data set into other data formats. The last two steps are performed only by user request.
This screenshot shows the input elements of the DAFU GUI. Here information about the source, target and final data sets is entered. It is important to set the type of the data set (ATKIS, TeleAtlas, Navteq or user-defined) and the file format (SVG, SHP, WKT, or user-defined). DAFU implements different sub-routines based on these settings. It is possible to set four different debugging levels (0 to 3). Once the input data has been entered successfully in the input section, the user can begin to analyse the input datasets. This is carried out in the attribute-section. The analysis of the various attributes and the values of the source and target data sets is important in order to determine which attributes should be included in the final data set.