Query: Mass-produced parts traceability system based on automated scanning of “Fingerprint of Things”

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Local Feature Based Multiple Object Instance Identification Using Scale and Rotation Invariant Implicit Shape Model <=SEP=> In this paper, we propose a Scale and Rotation Invariant Implicit Shape Model (SRIISM), and develop a local feature matching based system using the model to accurately locate and identify large numbers of object instances in an image. Due to repeated instances and cluttered background, conventional methods for multiple object instance identification suffer from poor identification results. In the proposed SRIISM, we model the joint distribution of object centers, scale, and orientation computed from local feature matches in Hough voting, which is not only invariant to scale changes and rotation of objects, but also robust to false feature matches. In the multiple object instance identification system using SRIISM, we apply a fast 4D bin search method in Hough space with complexity O(n), where n is the number of feature matches, in order to segment and locate each instance. Futhermore, we apply maximum likelihood estimation (MLE) for accurate object pose detection. In the evaluation, we created datasets simulating various industrial applications such as pick-and-place and inventory management. Experiment results on the datasets show that our method outperforms conventional methods in both accuracy (5%-30% gain) and speed (2x speed up).

Video Google: A Text Retrieval Approach to Object Matching in Videos <=SEP=> We describe an approach to object and scene retrieval which searches for and localizes all the occurrences of a user outlined object in a video. The object is represented by a set of viewpoint invariant region descriptors so that recognition can proceed successfully despite changes in viewpoint, illumination and partial occlusion. The temporal continuity of the video within a shot is used to track the regions in order to reject unstable regions and reduce the effects of noise in the descriptors. The analogy with text retrieval is in the implementation where matches on descriptors are pre-computed (using vector quantization), and inverted file systems and document rankings are used. The result is that retrieval is immediate, returning a ranked list of key frames/shots in the manner of Google. The method is illustrated for matching on two full length feature films.

Multi-image matching using multi-scale oriented patches <=SEP=> This paper describes a novel multi-view matching framework based on a new type of invariant feature. Our features are located at Harris corners in discrete scale-space and oriented using a blurred local gradient. This defines a rotationally invariant frame in which we sample a feature descriptor, which consists of an 8 /spl times/ 8 patch of bias/gain normalised intensity values. The density of features in the image is controlled using a novel adaptive non-maximal suppression algorithm, which gives a better spatial distribution of features than previous approaches. Matching is achieved using a fast nearest neighbour algorithm that indexes features based on their low frequency Haar wavelet coefficients. We also introduce a novel outlier rejection procedure that verifies a pairwise feature match based on a background distribution of incorrect feature matches. Feature matches are refined using RANSAC and used in an automatic 2D panorama stitcher that has been extensively tested on hundreds of sample inputs.

Robust Object Detection with Interleaved Categorization and Segmentation <=SEP=> This paper presents a novel method for detecting and localizing objects of a visual category in cluttered real-world scenes. Our approach considers object categorization and figure-ground segmentation as two interleaved processes that closely collaborate towards a common goal. As shown in our work, the tight coupling between those two processes allows them to benefit from each other and improve the combined performance. The core part of our approach is a highly flexible learned representation for object shape that can combine the information observed on different training examples in a probabilistic extension of the Generalized Hough Transform. The resulting approach can detect categorical objects in novel images and automatically infer a probabilistic segmentation from the recognition result. This segmentation is then in turn used to again improve recognition by allowing the system to focus its efforts on object pixels and to discard misleading influences from the background. Moreover, the information from where in the image a hypothesis draws its support is employed in an MDL based hypothesis verification stage to resolve ambiguities between overlapping hypotheses and factor out the effects of partial occlusion. An extensive evaluation on several large data sets shows that the proposed system is applicable to a range of different object categories, including both rigid and articulated objects. In addition, its flexible representation allows it to achieve competitive object detection performance already from training sets that are between one and two orders of magnitude smaller than those used in comparable systems.

Real-time object detection and localization with SIFT-based clustering <=SEP=> a r t i c l e i n f o Keywords: Pick-and-place applications Machine vision for industrial applications SIFT This paper presents an innovative approach for detecting and localizing duplicate objects in pick-and-place applications under extreme conditions of occlusion, where standard appearance-based approaches are likely to be ineffective. The approach exploits SIFT keypoint extraction and mean shift clustering to partition the correspondences between the object model and the image onto different potential object instances with real-time performance. Then, the hypotheses of the object shape are validated by a projection with a fast Euclidean transform of some delimiting points onto the current image. Moreover, in order to improve the detection in the case of reflective or transparent objects, multiple object models (of both the same and different faces of the object) are used and fused together. Many measures of efficacy and efficiency are provided on random disposals of heavily-occluded objects, with a specific focus on real-time processing. Experimental results on different and challenging kinds of objects are reported. Information technologies have become in the last decades a fundamental aid for helping the automation of everyday people life and industrial processes. Among the many different disciplines contributing to this process, machine vision and pattern recognition have been widely used for industrial applications and especially for robot vision. A typical need is to automate the pick-and-place process of picking up objects, possibly performing some tasks, and then placing them down on a different location. Most of the pick-and-place systems are basically composed of robotic systems and sensors. The sensors are in charge of driving the robot arms to the right 3D location and possibly orientation of the next object to be picked up, according to the robot's degrees of freedom. Object picking can be very complicated if the scene is not well structured and constrained. The automation of object picking by using cameras, however, requires to detect and localize objects in the scene; they are crucial tasks for several other computer vision applications, such as image/ video retrieval [1,2], or automatic robot navigation [3]. This paper describes a new complete approach for pick-and-place processes with the following challenging requirements: 1. Different types of objects: the approach should work with every type of object of different dimension and complexity, with reflective surfaces or semi-transparent parts, such as in the case of pharmaceutical and cosmetic objects, often reflective or included in transparent flowpacks; 2. …

Using Screen Brightness to Improve Security in Mobile Social Network Access <=SEP=> In the today's mobile communications scenario, smartphones offer new capabilities to develop sophisticated applications that seem to make daily life easier and more convenient for users. Such applications, which may involve mobile ticketing, identification, access control operations, etc., are often accessible through social network aggregators, that assume a fundamental role in the federated identity management space. While this makes modern smartphones very powerful devices, it also makes them very attractive targets for spyware injection. This kind of malware is able to bypass classic authentication measures and steal user credentials even when a secure element is used, and can, therefore, perform unauthorized mobile access to social network services without the user's consent. Such an event allows stealing sensitive information or even a full identity theft. In this work, we address this issue by introducing BrightPass, a novel authentication mechanism based on screen brightness. BrightPass allows users to authenticate safely with a PIN-based confirmation in the presence of specific operations on sensitive data. We compare BrightPass with existing schemes, in order to show its usability and security within the social network arena. Furthermore, we empirically assess the security of BrightPass through experimentation. Our tests indicate that BrightPass protects the PIN code against automatic submissions carried out by malware while granting fast authentication phases and reduced error rates.

Extensibility, Safety and Performance in the SPIN Operating System <=SEP=> This paper describes the motivation architecture and performance of SPIN an extensible operating system SPIN provides an extension infrastructure together with a core set of extensible services that allow applica tions to safely change the operating system s interface and implementation Extensions allow an application to specialize the underlying operating system in order to achieve a particular level of performance and function ality SPIN uses language and link time mechanisms to inexpensively export ne grained interfaces to operat ing system services Extensions are written in a type safe language and are dynamically linked into the op erating system kernel This approach o ers extensions rapid access to system services while protecting the op erating system code executing within the kernel address space SPIN and its extensions are written in Modula and run on DEC Alpha workstations

Feature hashing for large scale multitask learning <=SEP=> Empirical evidence suggests that hashing is an effective strategy for dimensionality reduction and practical nonparametric estimation. In this paper we provide exponential tail bounds for feature hashing and show that the interaction between random subspaces is negligible with high probability. We demonstrate the feasibility of this approach with experimental results for a new use case --- multitask learning with hundreds of thousands of tasks.

A new approach to unwrap a 3-D fingerprint to a 2-D rolled equivalent fingerprint <=SEP=> For many years, fingerprints have been captured by pressing a finger against a paper or hard surface. This touch-based fingerprint acquisition introduces some problems such as distortions and deformations in the acquired images, which arise due to the contact of the fingerprint surface with the sensor platen, and degrades the recognition performance. A new touch-less fingerprint technology has been recently introduced to the market, which can address the problems with the contact-based fingerprint systems. In this paper, we propose a new algorithm for unwrapping the acquired 3-D scan of the subject's finger into a 2-D rolled equivalent image. Therefore, The resulting image can be matched with the conventional 2-D scans; it also can be used for matching unwrapped 3-D fingerprints among themselves with the 2-D fingerprint matching algorithms. The algorithm is based on curvature analysis of the 3-D surface. The quality of the resulting image is evaluated and analyzed using NIST fingerprint image software.

Functional Search-based Testing from State Machines <=SEP=> The application of metaheuristic search techniques in test data generation has been extensively investigated in recent years. Most studies, however, have concentrated on the application of such techniques in structural testing. The use of search-based techniques in functional testing is less frequent, the main cause being the implicit nature of the specification. This paper investigates the use of search-based techniques for functional testing, having the specification in form of a state machine. Its purpose is to generate input data for chosen paths in a state machine, so that the parameter values provided to the methods satisfy the corresponding guards and trigger the desired transitions. A general form of a fitness function for an individual path is presented and this approach is empirically evaluated using three search techniques: simulated annealing, genetic algorithms and particle swarm optimization.

Imperative Functional Programming <=SEP=> We present a new model, based on monads, for performing input/output in a non-strict, purely functional language. It is composable, extensible, efficient, requires no extensions to the type system, and extends smoothly to incorporate mixed-language working and in-place array updates.

Ensemble Pruning Using Reinforcement Learning <=SEP=> Multiple Classifier systems have been developed in order to improve classification accuracy using methodologies for effective classifier combination. Classical approaches use heuristics, statistical tests, or a meta-learning level in order to find out the optimal combination function. We study this problem from a Reinforcement Learning perspective. In our modeling, an agent tries to learn the best policy for selecting classifiers by exploring a state space and considering a future cumulative reward from the environment. We evaluate our approach by comparing with state-of-the-art combination methods and obtain very promising results.

Improved ID3 algorithm <=SEP=> as the classical algorithm of the decision tree classification algorithm, ID3 is famous for the merits of high classifying speed easy, strong learning ability and easy construction. But when use it to classify, there does exist the problem of inclining to chose attributions which has many values, which affects its practicality. This paper for solving the problem a decision tree algorithm based on attribute-importance is proposed. The improved algorithm uses attribute-importance to increase information gain of attribution which has fewer attributions and compares ID3 with improved ID3 by an example. The experimental analysis of the data show that the improved ID3 algorithm can get more reasonable and more effective rules.

Convolutional Neural Network Information Fusion based on Dempster-Shafer Theory for Urban Scene Understanding <=SEP=> Dempster-Shafer theory provides a sensor fusion framework that autonomously accounts for obstacle occlusion in dynamic, urban environments. However, to discern static and moving obstacles, the Dempster-Shafer approach requires manual tuning of parameters dependent on the situation and sensor types. The proposed methodology utilizes a deep fully convolutional neural network to improve the robust performance of the information fusion algorithm in distinguishing static and moving obstacles from navigable space. The image-like spatial structure of probabilistic occupancy allows a semantic segmentation framework to discern classes for individual grid cells. A subset of the KITTI LIDAR tracking dataset in combination with semantic map data was used for the information fusion task. The probabilistic occupancy grid output of the Dempster-Shafer information fusion algorithm was provided as input to the neural network. The network then learned an offset from the original DST result to improve semantic labeling performance. The proposed framework outperformed the baseline approach in the mean intersection over union metric reaching 0.546 and 0.531 in the validation and test sets respectively. However, little improvement was achieved in discerning moving and static cells due to the limited dataset size. To improve model performance in future work, the dataset will be expanded to facilitate more effective learning, and temporal data will be fed through individual convolutional networks prior to being merged in channels as input to the main network.

MARVEL: A Large-Scale Image Dataset for Maritime Vessels <=SEP=> 

Baseline Mechanisms for Enterprise Governance of IT in SMEs <=SEP=> Information Technology (IT) has become fundamental for most organizations since it is vital to their sustainability, development and success. This pervasive use led organizations to a critical dependency on IT. Despite the benefits, it exposes organizations to several risks. Hence, a significant focus on Enterprise Governance of IT (EGIT) is required. EGIT involve the definition and implementation of processes, structures and relational mechanisms to support the business/IT alignment and the creation of business value from IT investments. These mechanisms act as a mean to direct and operationalize IT-related decision-making. However, identifying the appropriate mechanisms is a complex task since there are internal and external contingency factors that influence the design and implementation of EGIT. Small and Medium Enterprises (SMEs) are considered key elements to promote economic growth, job creation, social integration and innovation. The use of IT in these organizations can imply severe consequences on survival and growth in highly competitive markets, becoming critical in this globalization era. Several studies were developed to investigate and identify EGIT mechanisms in different contingencies but very few focused on the organization' size criterion. However, SMEs have particular characteristics that require further investigation. Therefore, our goal in this research is to evaluate EGIT mechanisms in a SME's context and identify a baseline of mechanisms for SMEs using semi-structured interviews with IT experts that have experience in SMEs. The article ends by presenting contributions, limitations and future work.

Operability of Folded Microstrip Patch-Type Tag Antenna in the UHF RFID Bands Within 865-928 MHz <=SEP=> The emerging use of passive radio frequency identification (RFID) systems at ultra-high-frequency (UHF) spectrum requires application specific tag antenna designs for challenging applications. Many objects containing conductive material need novel tag antenna designs for reliable identification. The operability of folded microstrip patch-type tag antenna for objects containing conductive material is analyzed and tested within the UHF RFID bands used at the moment mainly in Europe and in North and South America. First the operability of the tag antenna design and the effects of conductive material are modeled with simulations based on finite element method (FEM). The performance of the tag antenna design affixed to a package containing metallic foil is verified with read range measurements. The results show that the antenna design is operable in both of the UHF RFID bands within 865-928 MHz

Lessons from the journey: a query log analysis of within-session learning <=SEP=> The Internet is the largest source of information in the world. Search engines help people navigate the huge space of available data in order to acquire new skills and knowledge. In this paper, we present an in-depth analysis of sessions in which people explicitly search for new knowledge on the Web based on the log files of a popular search engine. We investigate within-session and cross-session developments of expertise, focusing on how the language and search behavior of a user on a topic evolves over time. In this way, we identify those sessions and page visits that appear to significantly boost the learning process. Our experiments demonstrate a strong connection between clicks and several metrics related to expertise. Based on models of the user and their specific context, we present a method capable of automatically predicting, with good accuracy, which clicks will lead to enhanced learning. Our findings provide insight into how search engines might better help users learn as they search.

Dual-Band Multi-Pole Directional Filter for Microwave Multiplexing Applications <=SEP=> A novel microstrip directional filter for multiplexing applications is presented. This device uses composite right-left-handed transmission lines and resonators to achieve dual-band frequency response. In addition, by cascading two or more stages using dual frequency immitance inverters, multi-pole configurations can be obtained. Simulation and experimental results are presented with good agreement.

Multi-domain spoken language understanding with transfer learning <=SEP=> This paper addresses the problem of multi-domain spoken language understanding (SLU) where domain detection and domaindependent semantic tagging problems are combined. We present a transfer learning approach to the multi-domain SLU problem in which multiple domain-specific data sources can be incorporated. To implement multi-domain SLU with transfer learning, we introduce a triangular-chain structured model. This model effectively learns multiple domains in parallel, and allows use of domain-independent patterns among domains to create a better model for the target domain. We demonstrate that the proposed method outperforms baseline models on dialog data for multi-domain SLU problems. 2009 Elsevier B.V. All rights reserved.

Can Computers Overcome Humans? Consciousness Interaction and its Implications <=SEP=> Can computers overcome human capabilities? This is a paradoxical and controversial question, particularly because there are many hidden assumptions. This article focuses on that issue putting on evidence some misconception related to future generations of machines and the understanding of the brain. It will discuss to what extent computers might reach human capabilities, and how it would be possible only if the computer is a conscious machine. However, it will be shown that if the computer is conscious, an interference process due to consciousness would affect the information processing of the system. Therefore, it might be possible to make conscious machines to overcome human capabilities, which will have similar limitations than humans. In other words, trying to overcome human capabilities with computers implies the paradoxical conclusion that a computer will never overcome human capabilities at all, or if the computer does, it should not be considered as a computer anymore.

Dynamic Facial Expression Recognition With Atlas Construction and Sparse Representation <=SEP=> In this paper, a new dynamic facial expression recognition method is proposed. Dynamic facial expression recognition is formulated as a longitudinal groupwise registration problem. The main contributions of this method lie in the following aspects: 1) subject-specific facial feature movements of different expressions are described by a diffeomorphic growth model; 2) salient longitudinal facial expression atlas is built for each expression by a sparse groupwise image registration method, which can describe the overall facial feature changes among the whole population and can suppress the bias due to large intersubject facial variations; and 3) both the image appearance information in spatial domain and topological evolution information in temporal domain are used to guide recognition by a sparse representation method. The proposed framework has been extensively evaluated on five databases for different applications: the extended Cohn-Kanade, MMI, FERA, and AFEW databases for dynamic facial expression recognition, and UNBC-McMaster database for spontaneous pain expression monitoring. This framework is also compared with several state-of-the-art dynamic facial expression recognition methods. The experimental results demonstrate that the recognition rates of the new method are consistently higher than other methods under comparison.

High-Speed Parallel Decimal Multiplication with Redundant Internal Encodings <=SEP=> The decimal multiplication is one of the most important decimal arithmetic operations which have a growing demand in the area of commercial, financial, and scientific computing. In this paper, we propose a parallel decimal multiplication algorithm with three components, which are a partial product generation, a partial product reduction, and a final digit-set conversion. First, a redundant number system is applied to recode not only the multiplier, but also multiples of the multiplicand in signed-digit (SD) numbers. Furthermore, we present a multioperand SD addition algorithm to reduce the partial product array. Finally, a digit-set conversion algorithm with a hybrid prefix network to decrease the number of the logic gates on the critical path is discussed. An analysis of the timing delay and an HDL model synthesized under 90 nm technology show that by considering the tradeoff of designs among three components, the overall delay of the proposed 16 × 16-digit multiplier takes about 11 percent less timing delay with 2 percent less area compared to the current fastest design.

Survey of Large-Scale MIMO Systems <=SEP=> The escalating teletraffic growth imposed by the proliferation of smartphones and tablet computers outstrips the capacity increase of wireless communications networks. Furthermore, it results in substantially increased carbon dioxide emissions. As a powerful countermeasure, in the case of full-rank channel matrices, MIMO techniques are potentially capable of linearly increasing the capacity or decreasing the transmit power upon commensurately increasing the number of antennas. Hence, the recent concept of large-scale MIMO (LS-MIMO) systems has attracted substantial research attention and been regarded as a promising technique for next-generation wireless communications networks. Therefore, this paper surveys the state of the art of LS-MIMO systems. First, we discuss the measurement and modeling of LS-MIMO channels. Then, some typical application scenarios are classified and analyzed. Key techniques of both the physical and network layers are also detailed. Finally, we conclude with a range of challenges and future research topics.

Physical Database Design: the database professional's guide to exploiting indexes, views, storage, and more <=SEP=> Designations used by companies to distinguish their products are often claimed as trademarks or registered trademarks. In all instances in which Morgan Kaufmann Publishers is aware of a claim, the product names appear in initial capital or all capital letters. Readers, however, should contact the appropriate companies for more complete information regarding trademarks and registration. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means—electronic, mechanical, photocopying, scanning, or otherwise—without prior written permission of the publisher. may also complete your request on-line via the Elsevier homepage (http://elsevier.com), by selecting " Support & Contact " then " Copyright and Permission " and then " Obtaining Permissions. " Page 197: " Make a new plan Stan. .. and get yourself free. Physical database design : the database professional's guide to exploiting indexes, views, storage, and more / Sam Lightstone, Toby Teorey, and Tom Nadeau. p. cm.-(The Morgan Kaufmann series in database management systems) Includes bibliographical references and index.

Area and length minimizing flows for shape segmentation <=SEP=> A number of active contour models have been proposed that unify the curve evolution framework with classical energy minimization techniques for segmentation, such as snakes. The essential idea is to evolve a curve (in two dimensions) or a surface (in three dimensions) under constraints from image forces so that it clings to features of interest in an intensity image. The evolution equation has been derived from first principles as the gradient flow that minimizes a modified length functional, tailored to features such as edges. However, because the flow may be slow to converge in practice, a constant (hyperbolic) term is added to keep the curve/surface moving in the desired direction. We derive a modification of this term based on the gradient flow derived from a weighted area functional, with image dependent weighting factor. When combined with the earlier modified length gradient flow, we obtain a partial differential equation (PDE) that offers a number of advantages, as illustrated by several examples of shape segmentation on medical images. In many cases the weighted area flow may be used on its own, with significant computational savings.

Research Advances in Cloud Computing <=SEP=> Serverless computing has emerged as a new compelling paradigm for the deployment of applications and services. It represents an evolution of cloud programmingmodels, abstractions, and platforms, and is a testament to the maturity and wide adoption of cloud technologies. In this chapter, we survey existing serverless platforms from industry, academia, and open-source projects, identify key characteristics and use cases, and describe technical challenges and open problems. I. Baldini · P. Castro (B) · K. Chang · P. Cheng · S. Fink · N. Mitchell · V. Muthusamy · R. Rabbah · A. Slominski IBM Research, New York, USA e-mail: ioana@us.ibm.com P. Castro e-mail: castrop@us.ibm.com K. Chang e-mail: Kerry.Chang@ibm.com P. Cheng e-mail: perry@us.ibm.com S. Fink e-mail: sjfink@us.ibm.com N. Mitchell e-mail: nickm@us.ibm.com V. Muthusamy (B) e-mail: vmuthus@us.ibm.com R. Rabbah e-mail: rabbah@us.ibm.com A. Slominski (B) e-mail: aslom@us.ibm.com V. Ishakian (B) Bentley University, Waltham, USA e-mail: vishakian@bentley.edu P. Suter Two Sigma, New York, USA © Springer Nature Singapore Pte Ltd. 2017 S. Chaudhary et al. (eds.), Research Advances in Cloud Computing, DOI 10.1007/978-981-10-5026-8_1 1

Primed for Violence : The Role of Gender Inequality in Predicting Internal Conflict <=SEP=> We know, most notably through Ted Gurr’s research, that ethnic discrimination can lead to ethnopolitical rebellion–intrastate conflict. I seek to discover what impact, if any, gender inequality has on intrastate conflict. Although democratic peace scholars and others highlight the role of peaceful domestic behavior in predicting state behavior, many scholars have argued that a domestic environment of inequality and violenceFstructural and cultural violenceFresults in a greater likelihood of violence at the state and the international level. This project contributes to this line of inquiry and further tests the grievance theory of intrastate conflict by examining the norms of violence that facilitate a call to arms. And in many ways, I provide an alternative explanation for the significance of some of the typical economic measuresFthe greed theoryFbased on the link between discrimination, inequality, and violence. I test whether states characterized by higher levels of gender inequality are more likely to experience intrastate conflict. Ultimately, the basic link between gender inequality and intrastate conflict is confirmedFstates characterized by gender inequality are more likely to experience intrastate conflict, 1960–2001.

Sales Taxes and Internet Commerce∗ <=SEP=> We estimate the sensitivity of Internet retail purchasing to sales taxes using data from the eBay marketplace. Our first approach exploits the fact that seller locations are revealed only after buyers have expressed interest in an item by clicking on its listing. We use millions of location “surprises”to estimate price elasticities with respect to the charged sales tax. We then use aggregated data to estimate cross-state substitution parameters, and substitution between offl ine and online purchases, relying both on cross-sectional variation in state and local sales taxes, and on changes in these rates over time. We find substantial sensitivity to sales taxes. Using our item-level approach, we find an average price elasticity of around -2 for interested buyers. Using our aggregate approach, we find that a one percentage point increase in a state’s sales tax increases online purchases by state residents by just under two percent, but decreases their online purchases from home-state retailers by 3-4 percent. (JEL: D12, H20, H71, L81) ∗We are grateful to Glenn Ellison and many seminar participants for useful comments. We appreciate support from the Stanford Institute for Economic Policy Research and the Toulouse Network on Information Technology. Data access for this study was obtained under a consulting agreement between the Stanford authors (Einav, Knoepfle and Levin) and eBay Research. †Department of Economics, Stanford University, Stanford, CA 94305-6072 (Einav, Knoepfle, Levin), NBER (Einav and Levin), and eBay Labs (Sundaresan). Email: leinav@stanford.edu, knoepfle@stanford.edu, jdlevin@stanford.edu, nsundaresan@ebay.com.

Internet Web servers: workload characterization and performance implications <=SEP=> This paper presents a workload characterization study for Internet Web servers. Six different data sets are used in the study: three from academic environments, two from scientific research organizations, and one from a commercial Internet provider. These data sets represent three different orders of magnitude in server activity, and two different orders of magnitude in time duration, ranging from one week of activity to one year. The workload characterization focuses on the document type distribution, the document size distribution, the document referencing behavior, and the geographic distribution of server requests. Throughout the study, emphasis is placed on finding workload characteristics that are common to all the data sets studied. Ten such characteristics are identified. The paper concludes with a discussion of caching and performance issues, using the observed workload characteristics to suggest performance enhancements that seem promising for Internet Web servers.