Data Processing Operations Research Articles

Revisiting low-fidelity two-fluid models for gas–solids transport

Two-phase gas–solids transport models are widely utilized for process design and automation in a broad range of industrial applications. Some of these applications include proppant transport in gaseous fracking fluids, air/gas drilling hydraulics, coal-gasification reactors and food processing units. Systems automation and real time process optimization stand to benefit a great deal from availability of efficient and accurate theoretical models for operations data processing. However, modeling two-phase pneumatic transport systems accurately requires a comprehensive understanding of gas–solids flow behavior. In this study we discuss the prevailing flow conditions and present a low-fidelity two-fluid model equation for particulate transport. The model equations are formulated in a manner that ensures the physical flux term remains conservative despite the inclusion of solids normal stress through the empirical formula for modulus of elasticity. A new set of Roe–Pike averages are presented for the resulting strictly hyperbolic flux term in the system of equations, which was used to develop a Roe-type approximate Riemann solver. The resulting scheme is stable regardless of the choice of flux-limiter. The model is evaluated by the prediction of experimental results from both pneumatic riser and air-drilling hydraulics systems. We demonstrate the effect and impact of numerical formulation and choice of numerical scheme on model predictions. We illustrate the capability of a low-fidelity one-dimensional two-fluid model in predicting relevant flow parameters in two-phase particulate systems accurately even under flow regimes involving counter-current flow.

Penjadwalan Mesin dengan Menggunakan Algoritma Pembangkitan Jadwal Aktif dan Algoritma Penjadwalan Non-Delay untuk Produk Hydrotiller dan Hammermil pada CV. Cherry Sarana Agro

Fulfillment of all demands of consumers who come to the product is one thing that always wanted to be achieved by a company. These requests are not independent of the company's ability to manufacture certain products. CV Cherry Sarana Agro manufactures a wide range of agricultural equipment, one of which is the product hydrotiller and hammermil. Demand for both products are always in large numbers for each period, however, the company could not meet the entire demand. One of the main factors that led this small company's production capacity for these two products is not optimal scheduling of machines made by companies, causing many to be a queue on a particular machine so that the total process operating time becomes very large. Scheduling method is used to optimize the scheduling of machines working on the report of this practice is actively scheduling method and the method of non-delay scheduling. The data needed to perform scheduling with both of these methods is the data used machines, data processing operations and data processing time of operation. With these three data, can be compared to the actual scheduling done by the company with the scheduling is done using active scheduling method and the method of non-delay scheduling. The most optimal scheduling is obtained after comparing the three methods used are scheduling using the non-delay scheduling. This method was chosen because the resulting make span is much smaller than the two other methods. This method is well applied in the company because in addition to reducing the total processing time, can also increase production capacity, so that all requests can be met.Keywords: Active schedulling method, non-delay schedulling method, makespan, hydrotiller, hammermil

CONSTRUCTIVE MODEL OF ADAPTATION OF DATA STRUCTURES IN RAM. PART II. CONSTRUCTORS OF SCENARIOS AND ADAPTATION PROCESSES

Purpose.The second part of the paper completes presentation of constructive and the productive structures (CPS), modeling adaptation of data structures in memory (RAM). The purpose of the second part in the research is to develop a model of process of adaptation data in a RAM functioning in different hardware and software environments and scenarios of data processing. Methodology. The methodology of mathematical and algorithmic constructionism was applied. In this part of the paper, changes were developed the constructors of scenarios and adaptation processes based on a generalized CPS through its transformational conversions. Constructors are interpreted, specialized CPS. Were highlighted the terminal alphabets of the constructor scenarios in the form of data processing algorithms and the constructor of adaptation – in the form of algorithmic components of the adaptation process. The methodology involves the development of substitution rules that determine the output process of the relevant structures. Findings. In the second part of the paper, system is represented by CPS modeling adaptation data placement in the RAM, namely, constructors of scenarios and of adaptation processes. The result of the implementation of constructor of scenarios is a set of data processing operations in the form of text in the language of programming C#, constructor of the adaptation processes – a process of adaptation, and the result the process of adaptation – the adapted binary code of processing data structures. Originality. For the first time proposed the constructive model of data processing – the scenario that takes into account the order and number of calls to the various elements of data structures and adaptation of data structures to the different hardware and software environments. At the same the placement of data in RAM and processing algorithms are adapted. Constructionism application in modeling allows to link data models and algorithms for their processing with the performance criteria in the framework of unified approach and applied means. The developed models allow us to study the process of adaptation and control it. Practical value. The developed model and methods allow automatically changing the data placement in the RAM and their algorithmic connection depending on the operational requirements, the design features of the hardware and software operating environment.

Scintillation noise power spectrum and its impact on high-redshift 21-cm observations

Visibility scintillation resulting from wave propagation through the turbulent ionosphere can be an important sources of noise at low radio frequencies ($\nu\lesssim 200$ MHz). Many low frequency experiments are underway to detect the power spectrum of brightness temperature fluctuations of the neutral-hydrogen $21$-cm signal from the Epoch of Reionization (EOR: $12\gtrsim z\gtrsim 7$, $100\lesssim \nu \lesssim 175$ MHz). In this paper, we derive scintillation noise power-spectra in such experiments while taking into account the effects of typical data processing operations such as self-calibration and Fourier synthesis. We find that for minimally redundant arrays such as LOFAR and MWA, scintillation noise is of the same order of magnitude as thermal noise, has a spectral coherence dictated by stretching of the snapshot $uv$-coverage with frequency, and thus is confined to the well known wedge-like structure in the cylindrical ($2$-dimensional) power spectrum space. Compact, fully redundant ($d_{\rm core}\lesssim r_{\rm F} \approx 300$ m at $150$ MHz) arrays such as HERA and SKA-LOW (core) will be scintillation noise dominated at all baselines, but the spatial and frequency coherence of this noise will allow it to be removed along with spectrally smooth foregrounds.

Vespucci: A Free, Cross-Platform Tool for Spectroscopic Data Analysis and Imaging

Vespucci is a software application developed for imaging and analysis of hyperspectral datasets. Vespucci offers several advantages over other software packages, including a simple user interface with a small learning curve, no cost, and less restrictive licensing. Vespucci expands several analysis techniques including univariate imaging, principal components analysis, partial-least-squares regression, and vertex components analysis with endmember extraction, and k-means clustering. Additionally, Vespucci can perform a number of useful data-processing operations, including filtering, normalization, baseline correction, and background subtraction. Datasets that consist of spatial or temporal data with a corresponding digital signal, including spectroscopic images, mass spectrometric images, and X-ray diffraction data can be processed in this software. A few use cases for Raman and surface-enhanced Raman spectroscopies are provided. Vespucci is written in C++ and makes use of the MLPACK [3], Armadillo [9], Qt, and QCustomPlot libraries. Vespucci is a graphically-driven package that is designed with ease-of-use in mind and is equally capable to other available tools. Vespucci’s capabilities are extended by interfaces to Octave and R to allow existing research code to be run from a common environment. Additionally, Vespucci’s C++ classes can be used to construct more specialized programs when an application programming interface (API) is desired. The source code and a Windows binary distribution can be accessed at https://github.com/dpfoose/Vespucci.

Carbon monoxide total columns from SCIAMACHY 2.3 µm atmospheric reflectance measurements: towards a full-mission data product (2003–2012)

Abstract. We present a full-mission data product of carbon monoxide (CO) vertical column densities using the 2310–2338 nm SCIAMACHY reflectance measurements over clear-sky land scenes for the period January 2003–April 2012. The retrieval employs the SICOR algorithm, which will be used for operational data processing of the Sentinel-5 Precursor mission. The retrieval approach infers simultaneously carbon monoxide, methane and water vapour column densities together with a Lambertian surface albedo from individual SCIAMACHY measurements employing a non-scattering radiative transfer model. To account for the radiometric instrument degradation including the formation of an ice-layer on the 2.3 µm detector array, we consider clear-sky measurements over the Sahara as a natural calibration target. For these specific measurements, we spectrally calibrate the SCIAMACHY measurements and determine a spectral radiometric offset and the width of the instrument spectral response function as a function of time for the entire operational phase of the mission. We show that the smoothing error of individual clear-sky CO retrievals is less than ±1 ppb and thus this error contribution does not need to be accounted for in the validation considering the much higher retrieval noise. The CO data product is validated against measurements of ground-based Fourier transform infrared spectrometers at 27 stations of the NDACC-IRWG and TCCON network and MOZAIC/IAGOS aircraft measurements at 26 airports worldwide. Overall, we find a good agreement with TCCON measurements with a mean bias b = −1.2 ppb and a station-to-station bias with σ = 7.2 ppb. The negative sign of the bias means a low bias of SCIAMACHY CO with respect to TCCON. For the NDACC-IRWG network, we obtain a larger mean station bias of b = −9.2 ppb with σ = 8.1 ppb and for the MOZAIC/IAGOS measurements we find b = −6.4 ppb with σ = 5.6 ppb. The SCIAMACHY data set is subject to a small but significant bias trend of 1.47 ± 0.25 ppb yr−1. After trend correction, the bias with respect to MOZAIC/IAGOS observation is 2.5 ppb, with respect to TCCON measurements it is −4.6 ppb and with respect to NDACC-IRWG measurements −8.4 ppb. Hence, a discrepancy of 3.8 ppb remains between the global biases with NDACC-IRWG and TCCON, which is confirmed by directly comparing NDACC-IRWG and TCCON measurements. Generally, the scatter of the individual SCIAMACHY CO retrievals is high and dominated by large measurement noise. Hence, for practical usage of the data set, averaging of individual retrievals is required. As an example, we show that monthly mean SCIAMACHY CO retrievals, averaged separately over Northern and Southern Africa, reflect the spatial and temporal variability of biomass burning events in agreement with the global chemical transport model TM5.

Multivariate analysis of credit risk and bankruptcy research data: a bibliometric study involving different knowledge fields (1968–2014)

Bibliometrics are important evaluation tools to the scientific production. A bibliometric study was used to evaluate researches about credit risk and bankruptcy. Credit support technique studies on economic and social orders are relevant in this knowledge field. Therefore, the aim of the current study is to identify and describe the application of multivariate data analysis techniques to credit risk and bankruptcy scenarios. The herein presented data were collected in publications indexed to Thomson Reuters' Web of Science database between 1968 and 2014. The results corroborate information in the literature and in previous bibliometric reviews, as well as highlight other indications regarding the construction and development of research fields. Since the 1990's, the neural networks became relevant due to their increased use as study object in publications. However, both the discriminant analysis (J Finance 23(4):589---609, 1968. doi:10.2307/2978933) and the logistic regression (J Account Res 18(1):109---131, 1980. doi:10.2307/2490395) are still often used in researches, fact that shows the tendency to find articles using more than one technique or hybrid models, artificial intelligence techniques and complex computer systems. This field appears to be multidisciplinary in journals and Web Science databases involving the business and economy, operational research, management, mathematics, data processing, engineering and statistics fields. Another relevant discovery was the increased number of publications about this subject launched right after the 2008 crisis.

Wind turbine reliability estimation for different assemblies and failure severity categories

This study discusses the life-cycle analysis of wind turbines through the processing of operational data from three modern European wind farms. A methodology for supervisory control and data acquisition data processing has been developed combining previous research findings and experience from operational wind farms followed by statistical analysis of the results. The analysis was performed by dividing the wind turbine into assemblies and the failures events in severity categories. Depending on the failure severity category a different statistical methodology was applied, examining the reliability growth and the applicability of the ‘bathtub curve’ concept for wind turbine reliability analysis.

Correction of Stray-Light-Driven Interslot Radiometric Discrepancy (ISRD) Present in Radiometric Products of Geostationary Ocean Color Imager (GOCI)

The radiometric calibration of satellite data is critical in many environmental studies and applications that are based on remote sensing data. The Geostationary Ocean Color Imager (GOCI) has suffered from what is called an interslot radiometric discrepancy (ISRD), which creates clear inconsistency between the adjacent slots in GOCI Level 1B (L1B) radiometric products, the largest source of which is currently believed to be the stray light generated in the sensor instrument. Difficulties in removing the stray-light-driven anomalies are that the intensity and the spatial extent vary with time and location, depending on the reflectance of nearby bright targets, such as cloud and land. This paper proposes an image-based correction method that removes the stray-light-driven radiometric inflation without involving an independent reference so that the method can be used for GOCI operational data processing. First, the radiometric inflation pattern is characterized by independent sources, such as Moderate Resolution Imaging Spectrometer (MODIS) data, and the inflation pattern is modeled by the minimum noise fraction transform of the input data. The modeled inflation patterns in individual slots are then adjusted across the slots in such a way that the overall ISRD in all slot boundaries is minimized. The analysis shows that the stray-light-driven radiometric anomalies can be up to 20% of the normal signals in Bands 6 (680 nm) and 8 (865 nm) of the uncorrected L1B images, and the proposed correction method reduces it to less than 2% in most of the cases, recovering the spatial continuity of natural variability across the slots.

A survey about job satisfaction and loyalty of data processing and control operators in Şişli Hamidiye Etfal Education and Research Hospital

A survey about job satisfaction and loyalty of data processing and control operators in Şişli Hamidiye Etfal Education and Research Hospital

Improving Nocturnal Fire Detection With the VIIRS Day–Night Band

Building on existing techniques for satellite remote sensing of fires, this paper takes advantage of the day–night band (DNB) aboard the Visible Infrared Imaging Radiometer Suite (VIIRS) to develop the Firelight Detection Algorithm (FILDA), which characterizes fire pixels based on both visible-light and infrared (IR) signatures at night. By adjusting fire pixel selection criteria to include visible-light signatures, FILDA allows for significantly improved detection of pixels with smaller and/or cooler subpixel hotspots than the operational Interface Data Processing System (IDPS) algorithm. VIIRS scenes with near-coincident Advanced Spaceborne Thermal Emission and Reflection (ASTER) overpasses are examined after applying the operational VIIRS fire product algorithm and including a modified “candidate fire pixel selection” approach from FILDA that lowers the 4- $\mu\text{m}$ brightness temperature (BT) threshold but includes a minimum DNB radiance. FILDA is shown to be effective in detecting gas flares and characterizing fire lines during large forest fires (such as the Rim Fire in California and High Park fire in Colorado). Compared with the operational VIIRS fire algorithm for the study period, FILDA shows a large increase (up to 90%) in the number of detected fire pixels that can be verified with the finer resolution ASTER data (90 m). Part (30%) of this increase is likely due to a combined use of DNB and lower 4- $\mu\text{m}$ BT thresholds for fire detection in FILDA. Although further studies are needed, quantitative use of the DNB to improve fire detection could lead to reduced response times to wildfires and better estimate of fire characteristics (smoldering and flaming) at night.

A Fast and Robust Scan-Line Search Algorithm for Object-to-Image Projection of AirbornePushbroom Images

AbstractThe projection of a 3D object point onto a 2D image plane (i.e., the object-to-image projection) is one of the fundamental operations in photogrammetric data processing. Unlike that for a frame image, the object-to-image projection for a pushbroom image is not a straightforward process because each scan line is exposed at a distinct instant of time, and a preliminary step is therefore required to determine the corresponding scan-line coordinate of the ground point to be projected. This paper presents a new scan-line search algorithm for the object-to-image projection of airborne pushbroom images, which employs a coarse-to-fine strategy following four consecutive steps: affine transformation, iterative search, sequential search, and sub-pixel inter-polation. The experimental results with 255 pushbroom images captured in various flight conditions show that the proposed algorithm is robust and can save at least 15 percent of the computational time when compared to the previous methods, while the latter often cannot yield correct results in strong turbulence scenarios.

An industry survey of the screw press system in palm oil mills: Operational data and malfunction issues

Research questionnaires were disseminated to active palm oil mills across Malaysia to acquire information on (i) the mill operation, and (ii) any malfunction issues concerning the screw press system. A total of 105 mills responded to the survey (response rate=25%). The relationship between the mill operational data (processing capacity, daily operation cycle), the screw press system (type, quantity), and the malfunction frequency was examined. The double screw press machine dominated the palm oil milling industries due to its larger capacity that entails a shorter processing time in comparison with the single screw press. Recurring malfunction problems were prevalent in both types of screw press system. While fatigue, corrosion, wear and tear are inevitable due to the heavy usage and natural causes, an irregular maintenance schedule and poor handling topped the list of common cause for malfunction of the screw press.

Analysis of transient and hysteresis behavior of cross-flow heat exchangers under variable fluid mass flow rate for data center cooling applications

Effective thermal management of data centers is an important aspect of reducing the energy required for the reliable operation of data processing and communications equipment. Liquid and hybrid (air/liquid) cooling approaches are becoming more widely used in today's large and complex data center facilities. Examples of these approaches include rear door heat exchangers, in-row and overhead coolers and direct liquid cooled servers. Heat exchangers are primary components of liquid and hybrid cooling systems, and the effectiveness of a heat exchanger strongly influences the thermal performance of a cooling system. Characterizing and modeling the dynamic behavior of heat exchangers is important for the design of cooling systems, especially for control strategies to improve energy efficiency. In this study, a dynamic thermal model is solved numerically in order to predict the transient response of an unmixed–unmixed crossflow heat exchanger, of the type that is widely used in data center cooling equipment. The transient response to step and ramp changes in the mass flow rate of both the hot and cold fluid is investigated. Five model parameters are varied over specific ranges to characterize the transient performance. The parameter range investigated is based on available heat exchanger data. The thermal response to the magnitude, time period and initial and final conditions of the transient input functions is studied in detail. Also, the hysteresis associated with the fluid mass flow rate variation is investigated. The modeling results and performance data are used to analyze specific dynamic performance of heat exchangers used in practical data center cooling applications.

Understanding the variability in ground-based methods for retrieving canopy openness, gap fraction, and leaf area index in diverse forest systems

Leaf area index (LAI) is a primary descriptor of vegetation structure, function, and condition. It is a vegetation product commonly derived from earth observation data. Independently obtained ground-based LAI estimates are vital for global satellite product validation. Acceptable uncertainties of these estimates are guided by satellite product accuracy thresholds stipulated by the World Meteorological Organisation (WMO) and the Global Climate Observing System (GCOS). This study compared canopy openness, gap fraction and LAI estimates derived from ground-based instruments; the primary focus was to compare high- and low-resolution (HR and LR) digital hemispherical photography (DHP) to a terrestrial laser scanner (TLS), augmented with measurements using the LAI-2200 plant canopy analyser in a subset of plots. Additionally, three common DHP classification methods were evaluated including a manual supervised (S) classification, a global (G) binary automated threshold, and a two-corner (TC) automated threshold applied to mixed pixels only. Coincident measurements were collected across five diverse forest systems in Eastern Australia with LAI values ranging from 0.5 to 5.5. Canopy openness, gap fraction and LAI were estimated following standard operational field data collection and data processing protocols. A total of 75 method-to-method pairwise comparisons were conducted, out of which 37 had an RMSD≥0.5 LAI and 26 were significantly different (p<0.05). HR-DHP (S) and two-corner (TC) methods were in close agreement with LAI-2200 (LAI RMSD 0.18 and 0.19, respectively). Additionally, the supervised (S) and two-corner (TC) methods were in close agreement over all canopy openness and LAI levels, matching to within 6% (openness: RMSD 0.04, LAI: RMSD 0.19). The automated classification method (TC) demonstrated the potential to be used as a substitute for the manual (S) classification (openness and LAI not significantly different, p>0.75). Although TLS produced on average 55% higher openness and LAI than the HR-DHP (S) and (TC) classification methods, the strong coefficient of determination indicated the potential to calibrate these methods (R2=0.88 and 0.79, respectively). Overall, results demonstrate a level of variability typically above the targeted uncertainty levels stipulated by the WMO and GCOS for satellite product validation. Further instrument calibration of TLS and improved DHP image capture and processing methods are expected to reduce these uncertainties.

Similarity Joins: Their implementation and interactions with other database operators

Similarity Joins are extensively used in multiple application domains and are recognized among the most useful data processing and analysis operations. They retrieve all data pairs whose distances are smaller than a predefined threshold ε. While several standalone implementations have been proposed, very little work has addressed the implementation of Similarity Joins as physical database operators. In this paper, we focus on the study, design, implementation, and optimization of a Similarity Join database operator for metric spaces. We present DBSimJoin, a physical database operator that integrates techniques to: enable a non-blocking behavior, prioritize the early generation of results, and fully support the database iterator interface. The proposed operator can be used with multiple distance functions and data types. We describe the changes in each query engine module to implement DBSimJoin and provide details of our implementation in PostgreSQL. We also study ways in which DBSimJoin can be combined with other similarity and non-similarity operators to answer more complex queries, and how DBSimJoin can be used in query transformation rules to improve query performance. The extensive performance evaluation shows that DBSimJoin significantly outperforms alternative approaches and scales very well when important parameters like ε, data size, and number of dimensions increase.

Perancangan Sistem Informasi Manajemen pada Klinik Skala Kecil (Studi Kasus: Klinik Dr. Jonni)

The purpose of this research was to develop a database application at dr. Jonni clinic which can be used for processing and recording of operational data with an attractive user interface. Through the integrated database application, data on dr. Jonni clinic would be more structured, therefore data retrieval will go faster and more accurate, data security administration will be increased, data redundancy will be eliminated and service performance clinic dr. Jonni will also be increased. The method of analysis includes the study of literature, examination of documentation, observation, and interviews. While in the design, the method includes application design using the waterfall method models, database design and data modeling application screens using State Transition Diagrams. The results achieved from this research is an application of data base management hospital. With database applications, all data and information can be integrated well, so that it would improve the quality of services as well as facilitate storing and accessing data. The conclusions obtained from this research is the application of data base management hospital helps in generating quick and accurate information that is used to support operations and decision-making in the clinic dr. Jonni.

Using Meta-mining to Support Data Mining Workflow Planning and Optimization

Knowledge Discovery in Databases is a complex process that involves many different data processing and learning operators. Today's Knowledge Discovery Support Systems can contain several hundred operators. A major challenge is to assist the user in designing workflows which are not only valid but also -- ideally -- optimize some performance measure associated with the user goal. In this paper we present such a system. The system relies on a meta-mining module which analyses past data mining experiments and extracts meta-mining models which associate dataset characteristics with workflow descriptors in view of workflow performance optimization. The meta-mining model is used within a data mining workflow planner, to guide the planner during the workflow planning. We learn the meta-mining models using a similarity learning approach, and extract the workflow descriptors by mining the workflows for generalized relational patterns accounting also for domain knowledge provided by a data mining ontology. We evaluate the quality of the data mining workflows that the system produces on a collection of real world datasets coming from biology and show that it produces workflows that are significantly better than alternative methods that can only do workflow selection and not planning.

PREDIKCE CHOVÁNÍ ZÁKAZNÍKA PROSTŘEDKY DATAMININGU

Business managers accounting for commercial success or non-success of the organization have to gain knowledge needful for correct decision acceptance. These knowledge represent sophisticated information hidden in enterprise data. One possibility, how to extract mentioned knowledge from data, is to use so-called datamining assets.The paper deals with an application of chosen basic methods of knowledge discovering in da­ta­ba­ses for area of customer-provider relation and it presents, how to avail acquired knowledge as basis of managerial decisions leading to improving of customer relationship management. It solves prediction, whose aim is, on the basis of some attributes of exploring objects, to predict future be­ha­viour of objects with these attributes. This way acquired knowledge, as the output of prediction, then can markedly help competent enterprise manager with planning of marketing strategies, for example so-called cross-selling and up-selling. The contribution describes a whole operation of available data processing: from its purifying, over its preparation for mining task, to self processing by the help of SAS Enterprise Miner tool. Regression analysis, neural network and decision tree, whose principles are briefly explained in this paper too, were used for knowledge mining. The estimation of customer behaviour was tested by two mining task varying in attribute using and in categories number of one of predicive attributes. The results of these two tasks are confronted by the help of prediction fruitfulness charts.

An efficient graph data processing system for large‐scale social network service applications

SummaryTrust in social network draws more and more attentions from both the academia and industry fields. Public opinion analysis is a direct way to increase the trust in social network. Because the public opinion analysis can be expressed naturally by the graph algorithm and graph data are the default data organization mechanism used in large‐scale social network service applications, more and more research works apply the graph processing system to deal with the public opinion analysis. As the data volume is growing rapidly, the distributed graph systems are introduced to process the large‐scale public opinion analysis. Most of graph algorithms introduce a large number of data iterations, so the synchronization requirements between successive iterations can severely jeopardize the effectiveness of parallel operations, which makes the data aggregation and analysis operations become slower. In this paper, we propose a large‐scale graph data processing system to address these issues, which includes a graph data processing model, Arbor. Arbor develops a new graph data organization format to represent the social relationship, and the format can not only save storage space but also accelerate graph data processing operations. Furthermore, Arbor substitutes time‐constrained synchronization operations with non‐time‐constrained control message transmissions to increase the degree of parallelism. Based on the system, we put forward two most frequently used graph applications on Arbor: shortest path and PageRank. In order to evaluate the system, we compare Arbor with the other graph processing systems using large‐scale experimental graph data, and the results show that it outperforms the state‐of‐the‐art systems. Copyright © 2014 John Wiley &amp; Sons, Ltd.