Search results for: Data Base

Authors: Kanyarat Bussaban, Kunyanuth Kularbphettong

Abstract:

This research aims to create a model for analysis of student behavior using Library resources based on data mining technique in case of Suan Sunandha Rajabhat University. The model was created under association rules, Apriori algorithm. The results were found 14 rules and the rules were tested with testing data set and it showed that the ability of classify data was 79.24percent and the MSE was 22.91. The results showed that the user’s behavior model by using association rule technique can use to manage the library resources.

Keywords: Behavior, data mining technique, Apriori algorithm.

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Authors: T. Thulare, M. Herselman, A. Botha

Abstract:

Poor system use, including inappropriate design of health information systems, causes difficulties in communication with patients and increased time spent by healthcare professionals in recording the necessary health information for medical records. System features like pop-up reminders, complex menus, and poor user interfaces can make medical records far more time consuming than paper cards as well as affect decision-making processes. Although errors associated with health information and their real and likely effect on the quality of care and patient safety have been documented for many years, more research is needed to measure the occurrence of these errors and determine the causes to implement solutions. Therefore, the purpose of this paper is to identify data integrity challenges in hospital information systems through a scoping review and based on the results provide recommendations on how to manage these. Only 34 papers were found to be most suitable out of 297 publications initially identified in the field. The results indicated that human and computerized systems are the most common challenges associated with data integrity and factors such as policy, environment, health workforce, and lack of awareness attribute to these challenges but if measures are taken the data integrity challenges can be managed.

Keywords: Data integrity, data integrity challenges, hospital information systems, South Africa.

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Authors: Sean Paulsen, Michael Casey

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In this work, we present a self-supervised pretraining framework for transformers on functional Magnetic Resonance Imaging (fMRI) data. First, we pretrain our architecture on two self-supervised tasks simultaneously to teach the model a general understanding of the temporal and spatial dynamics of human auditory cortex during music listening. Our pretraining results are the first to suggest a synergistic effect of multitask training on fMRI data. Second, we finetune the pretrained models and train additional fresh models on a supervised fMRI classification task. We observe significantly improved accuracy on held-out runs with the finetuned models, which demonstrates the ability of our pretraining tasks to facilitate transfer learning. This work contributes to the growing body of literature on transformer architectures for pretraining and transfer learning with fMRI data, and serves as a proof of concept for our pretraining tasks and multitask pretraining on fMRI data.

Keywords: Transfer learning, fMRI, self-supervised, brain decoding, transformer, multitask training.

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Authors: Xiaohua Liu, Juan F. Beltran, Nishant Mohanchandra, Godfried T. Toussaint

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Support vector machines (SVMs) are considered to be the best machine learning algorithms for minimizing the predictive probability of misclassification. However, their drawback is that for large data sets the computation of the optimal decision boundary is a time consuming function of the size of the training set. Hence several methods have been proposed to speed up the SVM algorithm. Here three methods used to speed up the computation of the SVM classifiers are compared experimentally using a musical genre classification problem. The simplest method pre-selects a random sample of the data before the application of the SVM algorithm. Two additional methods use proximity graphs to pre-select data that are near the decision boundary. One uses k-Nearest Neighbor graphs and the other Relative Neighborhood Graphs to accomplish the task.

Keywords: Machine learning, data mining, support vector machines, proximity graphs, relative-neighborhood graphs, k-nearestneighbor graphs, random sampling, training data condensation.

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Authors: Jun Wan, Zehua Chen, Yingwu Chen, Zhidong Bai

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This paper proposes a novel feature extraction method, based on Discrete Wavelet Transform (DWT) and K-L Seperability (KLS), for the classification of Functional Data (FD). This method combines the decorrelation and reduction property of DWT and the additive independence property of KLS, which is helpful to extraction classification features of FD. It is an advanced approach of the popular wavelet based shrinkage method for functional data reduction and classification. A theory analysis is given in the paper to prove the consistent convergence property, and a simulation study is also done to compare the proposed method with the former shrinkage ones. The experiment results show that this method has advantages in improving classification efficiency, precision and robustness.

Keywords: classification, functional data, feature extraction, K-Lseperability, wavelet.

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Authors: M. Pandi, K. Premalatha

Abstract:

A DNA microarray technology is a collection of microscopic DNA spots attached to a solid surface. Scientists use DNA microarrays to measure the expression levels of large numbers of genes simultaneously or to genotype multiple regions of a genome. Elucidating the patterns hidden in gene expression data offers a tremendous opportunity for an enhanced understanding of functional genomics. However, the large number of genes and the complexity of biological networks greatly increase the challenges of comprehending and interpreting the resulting mass of data, which often consists of millions of measurements. It is handled by clustering which reveals the natural structures and identifying the interesting patterns in the underlying data. In this paper, gene based clustering in gene expression data is proposed using Cuckoo Search with Differential Evolution (CS-DE). The experiment results are analyzed with gene expression benchmark datasets. The results show that CS-DE outperforms CS in benchmark datasets. To find the validation of the clustering results, this work is tested with one internal and one external cluster validation indexes.

Keywords: DNA, Microarray, genomics, Cuckoo Search, Differential Evolution, Gene expression data, Clustering.

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Authors: Dmitry Yu. Korulkin, Raissa A. Muzychkina, Evgenii N. Kojaev

Abstract:

In review the generalized data about biological activity of anthraquinone-containing plants and specimens on their basis is presented. Data of traditional medicine, results of bioscreening and clinical researches of specimens are analyzed.

Keywords: Anthraquinones, physiologically active substances, phytopreparation, Ramon.

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Authors: Carla Layana Luis Diambra

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Microarrays technique allows the simultaneous measurements of the expression levels of thousands of mRNAs. By mining this data one can identify the dynamics of the gene expression time series. By recourse of principal component analysis, we uncover the circadian rhythmic patterns underlying the gene expression profiles from Cyanobacterium Synechocystis. We applied PCA to reduce the dimensionality of the data set. Examination of the components also provides insight into the underlying factors measured in the experiments. Our results suggest that all rhythmic content of data can be reduced to three main components.

Keywords: circadian rhythms, clustering, gene expression, PCA.

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Authors: S. Soommat, S. Patamatamkul, T. Prempridi, M. Sritulyachot, P. Ineure, S. Yimman

Abstract:

Currently, slider process of Hard Disk Drive Industry become more complex, defective diagnosis for yield improvement becomes more complicated and time-consumed. Manufacturing data analysis with data mining approach is widely used for solving that problem. The existing mining approach from combining of the KMean clustering, the machine oriented Kruskal-Wallis test and the multivariate chart were applied for defective diagnosis but it is still be a semiautomatic diagnosis system. This article aims to modify an algorithm to support an automatic decision for the existing approach. Based on the research framework, the new approach can do an automatic diagnosis and help engineer to find out the defective factors faster than the existing approach about 50%.

Keywords: Slider process, Defective diagnosis and Data mining.

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Authors: Osamah M. Al-Qershi, Khoo Bee Ee

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In recent years image watermarking has become an important research area in data security, confidentiality and image integrity. Many watermarking techniques were proposed for medical images. However, medical images, unlike most of images, require extreme care when embedding additional data within them because the additional information must not affect the image quality and readability. Also the medical records, electronic or not, are linked to the medical secrecy, for that reason, the records must be confidential. To fulfill those requirements, this paper presents a lossless watermarking scheme for DICOM images. The proposed a fragile scheme combines two reversible techniques based on difference expansion for patient's data hiding and protecting the region of interest (ROI) with tamper detection and recovery capability. Patient's data are embedded into ROI, while recovery data are embedded into region of non-interest (RONI). The experimental results show that the original image can be exactly extracted from the watermarked one in case of no tampering. In case of tampered ROI, tampered area can be localized and recovered with a high quality version of the original area.

Keywords: DICOM, reversible, ROI-based, watermarking.

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Authors: Samiah Alammari, Nassim Ammour

Abstract:

When providing a massive number of tasks successively to a deep learning process, a good performance of the model requires preserving the previous tasks data to retrain the model for each upcoming classification. Otherwise, the model performs poorly due to the catastrophic forgetting phenomenon. To overcome this shortcoming, we developed a successful continual learning deep model for remote sensing hyperspectral image regions classification. The proposed neural network architecture encapsulates two trainable subnetworks. The first module adapts its weights by minimizing the discrimination error between the land-cover classes during the new task learning, and the second module tries to learn how to replicate the data of the previous tasks by discovering the latent data structure of the new task dataset. We conduct experiments on hyperspectral image (HSI) dataset on Indian Pines. The results confirm the capability of the proposed method.

Keywords: Continual learning, data reconstruction, remote sensing, hyperspectral image segmentation.

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Authors: Mritha Ramalingam

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Computer technology and the Internet have made a breakthrough in the existence of data communication. This has opened a whole new way of implementing steganography to ensure secure data transfer. Steganography is the fine art of hiding the information. Hiding the message in the carrier file enables the deniability of the existence of any message at all. This paper designs a stego machine to develop a steganographic application to hide data containing text in a computer video file and to retrieve the hidden information. This can be designed by embedding text file in a video file in such away that the video does not loose its functionality using Least Significant Bit (LSB) modification method. This method applies imperceptible modifications. This proposed method strives for high security to an eavesdropper-s inability to detect hidden information.

Keywords: Data hiding, LSB, Stego machine, VideoSteganography

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Authors: Mikel Niño, Roberto V. Zicari, Todor Ivanov, Kim Hee, Naveed Mushtaq, Marten Rosselli, Concha Sánchez-Ocaña, Karsten Tolle, José Miguel Blanco, Arantza Illarramendi, Jörg Besier, Harry Underwood

Abstract:

One of the application fields for data analysis techniques and technologies gaining momentum is the area of social good or “common good”, covering cases related to humanitarian crises, global health care, or ecology and environmental issues, among others. The promotion of data-driven projects in this field aims at increasing the efficacy and efficiency of social initiatives, improving the way these actions help humanity in general and people in need in particular. This application field, however, poses its own barriers and challenges when developing data-driven projects, lagging behind in comparison with other scenarios. These challenges derive from aspects such as the scope and scale of the social issue to solve, cultural and political barriers, the skills of main stakeholders and the technological resources available, the motivation to be engaged in such projects, or the ethical and legal issues related to sensitive data. This paper analyzes the application of data projects in the field of social good, reviewing its current state and noteworthy initiatives, and presenting a framework covering the key aspects to analyze in such projects. The goal is to provide guidelines to understand the main challenges and opportunities for this type of data project, as well as identifying the main differential issues compared to “classical” data projects in general. A case study is presented on the initial steps and stakeholder analysis of a data project for the inclusion of refugees in the city of Frankfurt, Germany, in order to empirically confront the framework with a real example.

Keywords: Data-Driven projects, humanitarian operations, personal and sensitive data, social good, stakeholders analysis.

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Authors: Rohan Putatunda, Aryya Gangopadhyay

Abstract:

Acoustic sensors, if embedded in smart street lights, can help in capturing the activities (car honking, sirens, events, traffic, etc.) in cities. Needless to say, the acoustic data from such scenarios are complex due to multiple audio streams originating from different events, and when decomposed to independent signals, the amount of retrieved data volume is small in quantity which is inadequate to train deep neural networks. So, in this paper, we address the two challenges: a) separating the mixed signals, and b) developing an efficient acoustic classifier under data paucity. So, to address these challenges, we propose an architecture with supervised deep learning, where the initial captured mixed acoustics data are analyzed with Fast Fourier Transformation (FFT), followed by filtering the noise from the signal, and then decomposed to independent signals by fast independent component analysis (Fast ICA). To address the challenge of data paucity, we propose a multi feature-based deep neural network with high performance that is reflected in our experiments when compared to the conventional convolutional neural network (CNN) and multi-layer perceptron (MLP).

Keywords: FFT, ICA, vehicle classification, multi-feature DNN, CNN, MLP.

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Authors: Heba Mohammed Nagy, Walid Mohamed Aly, Osama Fathy Hegazy

Abstract:

Educational  data mining  is  a  specific  data   mining field applied to data originating from educational environments, it relies on different  approaches to discover hidden knowledge  from  the  available   data. Among these approaches are   machine   learning techniques which are used to build a system that acquires learning from previous data. Machine learning can be applied to solve different regression, classification, clustering and optimization problems.

In  our  research, we propose  a “Student  Advisory  Framework” that  utilizes  classification  and  clustering  to  build  an  intelligent system. This system can be used to provide pieces of consultations to a first year  university  student to  pursue a  certain   education   track   where  he/she  will  likely  succeed  in, aiming  to  decrease   the  high  rate   of  academic  failure   among these  students.  A real case study  in Cairo  Higher  Institute  for Engineering, Computer  Science  and  Management  is  presented using  real  dataset   collected  from  2000−2012.The dataset has two main components: pre-higher education dataset and first year courses results dataset. Results have proved the efficiency of the suggested framework.

Keywords: Classification, Clustering, Educational Data Mining (EDM), Machine Learning.

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Authors: Masoud Afrand, Amin Behzadmehr, Arash Karimipour

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The world demand for potable water is increasing every day with growing population. Desalination using solar energy is suitable for potable water production from brackish and seawater. In this paper, we present a theoretical study of solar distillation in a single basin under the open environmental conditions of Chabahar-Iran. The still has a base area of 2000mm×500mm with a glass cover inclined at 25° in order to obtain extra solar energy. We model the still and conduct its energy balance equations under minor assumptions. We computed the temperatures of glass cover, seawater interface, moist air and bottom using numerical method. The investigation addressed the following: The still productivity, distilled water salinity and still performance in terms of the still efficiency. Calculated still productivity in July was higher than December. So in this paper, we show that still productivity is directly functioning of solar radiation.

Keywords: Inclined Solar still, Solar energy, Solar desalination, Numerical Simulation.

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Authors: Lilac A. E. Al-Safadi

Abstract:

This paper proposes an auto-classification algorithm of Web pages using Data mining techniques. We consider the problem of discovering association rules between terms in a set of Web pages belonging to a category in a search engine database, and present an auto-classification algorithm for solving this problem that are fundamentally based on Apriori algorithm. The proposed technique has two phases. The first phase is a training phase where human experts determines the categories of different Web pages, and the supervised Data mining algorithm will combine these categories with appropriate weighted index terms according to the highest supported rules among the most frequent words. The second phase is the categorization phase where a web crawler will crawl through the World Wide Web to build a database categorized according to the result of the data mining approach. This database contains URLs and their categories.

Keywords: Information Processing on the Web, Data Mining, Document Classification.

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Authors: Andrzej Śluzek

Abstract:

To illustrate diversity of methods used to extract relevant (where the concept of relevance can be differently defined for different applications) visual data, the paper discusses three groups of such methods. They have been selected from a range of alternatives to highlight how hardware and software tools can be complementarily used in order to achieve various functionalities in case of different specifications of “relevant data". First, principles of gated imaging are presented (where relevance is determined by the range). The second methodology is intended for intelligent intrusion detection, while the last one is used for content-based image matching and retrieval. All methods have been developed within projects supervised by the author.

Keywords: Relevant visual data, gated imaging, intrusion detection, image matching.

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Authors: Ahmad Anzaar, Husain Shahnawaz, Chand Mukesh, S. C. Gupta, R. Gowri, H. L. Mandoria

Abstract:

Due to insufficient frequency band and tremendous growth of the mobile users, complex computation is needed for the use of resources. Long distance communication began with the introduction of telegraphs and simple coded pulses, which were used to transmit short messages. Since then numerous advances have rendered reliable transfer of information both easier and quicker. Wireless network refers to any type of computer network that is wireless, and is commonly associated with a telecommunications network whose interconnections between nodes is implemented without the use of wires. Wireless network can be broadly categorized in infrastructure network and infrastructure less network. Infrastructure network is one in which we have a base station to serve the mobile users and in the infrastructure less network is one in which no infrastructure is available to serve the mobile users this kind of networks are also known as mobile Adhoc networks. In this paper we have simulated the result for different scenarios with protocols like AODV and DSR; we simulated the result for throughput, delay and receiving traffic in the given scenario.

Keywords: Adhoc network, AODV, DSR. mobility.

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Authors: Farshid Fathinia, Mohammad Parsazadeh, Amirhossein Heshmati

Abstract:

Turbulent forced convection flow in a 2-dimensional channel over periodic grooves is numerically investigated. Finite volume method is used to study the effect of turbulence model. The range of Reynolds number varied from 10000 to 30000 for the ribheight to channel-height ratio (B/H) of 2. The downstream wall is heated by a uniform heat flux while the upstream wall is insulated. The investigation is analyzed with different types of nanoparticles such as SiO2, Al2O3, and ZnO, with water as a base fluid are used. The volume fraction is varied from 1% to 4% and the nanoparticle diameter is utilized between 20nm to 50nm. The results revealed 114% heat transfer enhancement compared to the water in a grooved channel by using SiO2 nanoparticle with volume fraction and nanoparticle diameter of 4% and 20nm respectively.

Keywords: Forced convection, Periodic grooves, Nanofluids, Turbulent model, Heat transfer.

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Authors: Jintana Khemprasit, Vatcharaporn Esichaikul

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Crime is a major societal problem for most of the world's nations. Consequently, the police need to develop new methods to improve their efficiency in dealing with these ever increasing crime rates. Two of the common difficulties that the police face in crime control are crime investigation and the provision of crime information to the general public to help them protect themselves. Crime control in police operations involves the use of spatial data, crime data and the related crime data from different organizations (depending on the nature of the analysis to be made). These types of data are collected from several heterogeneous sources in different formats and from different platforms, resulting in a lack of standardization. Moreover, there is no standard framework for crime data collection, integration and dissemination through mobile devices. An investigation into the current situation in crime control was carried out to identify the needs to resolve these issues. This paper proposes and investigates the use of service oriented architecture (SOA) and the mobile spatial information service in crime control. SOA plays an important role in crime control as an appropriate way to support data exchange and model sharing from heterogeneous sources. Crime control also needs to facilitate mobile spatial information services in order to exchange, receive, share and release information based on location to mobile users anytime and anywhere.

Keywords: Crime Control, Geographic Information System (GIS), Mobile GIS, Service Oriented Architecture (SOA).

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Authors: K. M. Doraiswamy, Lakshminarayana Merugu, B. C. Jinaga

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This paper explains the cause of nonlinearity in floor attenuation hither to left unexplained. The performance degradation occurring in air interface for GSM signals is quantitatively analysed using the concept of Radiating Columns of buildings. The signal levels were measured using Wireless Network Optimising Drive Test Tool (E6474A of Agilent Technologies). The measurements were taken in reflected signal environment under usual fading conditions on actual GSM signals radiated from base stations. A mathematical model is derived from the measurements to predict the GSM signal levels in different floors. It was applied on three buildings and found that the predicted signal levels deviated from the measured levels with in +/- 2 dB for all floors. It is more accurate than the prediction models based on Floor Attenuation Factor. It can be used for planning proper indoor coverage in multi storey buildings.

Keywords: GSM air interface, nonlinear attenuation, multistory building, radiating columns, ground conduction and floor attenuation factor.

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Authors: Nur Atiqah Rochin Demong, Jie Lu, Farookh Khadeer Hussain

Abstract:

Property investment in the real estate industry has a high risk due to the uncertainty factors that will affect the decisions made and high cost. Analytic hierarchy process has existed for some time in which referred to an expert-s opinion to measure the uncertainty of the risk factors for the risk analysis. Therefore, different level of experts- experiences will create different opinion and lead to the conflict among the experts in the field. The objective of this paper is to propose a new technique to measure the uncertainty of the risk factors based on multidimensional data model and data mining techniques as deterministic approach. The propose technique consist of a basic framework which includes four modules: user, technology, end-user access tools and applications. The property investment risk analysis defines as a micro level analysis as the features of the property will be considered in the analysis in this paper.

Keywords: Uncertainty factors, data mining, multidimensional data model, risk analysis.

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Authors: Michal Cerny

Abstract:

We consider linear regression models where both input data (the values of independent variables) and output data (the observations of the dependent variable) are interval-censored. We introduce a possibilistic generalization of the least squares estimator, so called OLS-set for the interval model. This set captures the impact of the loss of information on the OLS estimator caused by interval censoring and provides a tool for quantification of this effect. We study complexity-theoretic properties of the OLS-set. We also deal with restricted versions of the general interval linear regression model, in particular the crisp input – interval output model. We give an argument that natural descriptions of the OLS-set in the crisp input – interval output cannot be computed in polynomial time. Then we derive easily computable approximations for the OLS-set which can be used instead of the exact description. We illustrate the approach by an example.

Keywords: Linear regression, interval-censored data, computational complexity.

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Authors: Camelia Iuliana Lungu, Chirața Caraiani, Cornelia Dascălu

Abstract:

The business strategy of any company wanting to be competitive on the market should be designed around the concept of intangibles, with an increasingly decisive role in knowledge transfer of the biggest corporations. Advancing the research in these areas, this study integrates the two approaches, emphasizing the relationships between the components of intellectual capital and corporate social responsibility. The three dimensions of intellectual capital in terms of sustainability requirements are debated. The paper introduces the concept of sustainable intellectual capital and debates it within an assessment model designed on the base of key performance indicators. The results refer to the assessment of possible ways for including the information on intellectual capital and corporate responsibility within the corporate strategy. The conclusions enhance the need for companies to be ready to support the integration of this type of information the knowledge transfer process, in order to develop competitive advantage on the market.

Keywords: Corporate social responsibility, corporate strategy, intellectual capital, sustainability

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Authors: M Nayeripour, E. Azad, A. Roosta, T. Niknam

Abstract:

In this paper, the transformers over-load problem of Shiraz substation in Fars Regional Electric Company (FREC) is investigated for a period of three years plan. So the suggestions for using phase shifting transformer (PST) and unified power flow controller (UPFC) in order to solve this problem are examined in details and finally, some economical and practical designs will be given in order to solve the related problems. Practical consideration and using the basic and fundamental concept of powers in transmission lines in order to find the economical design are the main advantages of this research. The simulation results of the integrated overall system with different designs compare them base on economical and practical aspects to solve the over-load and loss-reduction.

Keywords: Congestion management, Phase shifting transformer(PST), Unified power flow controller (UPFC), Transmission lines.

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Authors: Soon-Hyun Park, Takami Matsuo

Abstract:

This paper presents an adaptive differentiator of sequential data based on the adaptive control theory. The algorithm is applied to detect moving objects by estimating a temporal gradient of sequential data at a specified pixel. We adopt two nonlinear intensity functions to reduce the influence of noises. The derivatives of the nonlinear intensity functions are estimated by an adaptive observer with σ-modification update law.

Keywords: Adaptive estimation, parameter adjustmentlaw, motion detection, temporal gradient, differential filter.

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Authors: Chaimongkol Limpianchob

Abstract:

This work addresses the problem of production planning that arises in the production of aromatic coconuts from Samudsakhorn province in Thailand. The planning involves the forwarding of aromatic coconuts from the harvest areas to the factory, which is classified into two groups; self-owned areas and contracted areas, the decisions of aromatic coconuts flow in the plant, and addressing a question of which warehouse will be in use. The problem is formulated as a mixed-integer linear programming model within supply chain management framework. The objective function seeks to minimize the total cost including the harvesting, labor and inventory costs. Constraints on the system include the production activities in the company and demand requirements. Numerical results are presented to demonstrate the feasibility of coconuts supply chain model compared with base case.

Keywords: Aromatic coconut, supply chain management, production planning, mixed-integer linear programming.

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Authors: Tero Huhtala, Minna Pikkarainen, Saila Saraniemi

Abstract:

Information technology has long been used as an enabler of exchange for goods and services. Services are evolving from generic to personalized, and the reverse use of customer data has been discussed in both academia and industry for the past few years. This article presents the results of an empirical case study in the area of preventive health care services. The primary data were gathered in workshops, in which future personal data-based services were conceptualized by analyzing future scenarios from a business perspective. The aim of this study is to understand business model transformation in emerging personal data ecosystems. The work was done as a case study in the context of occupational healthcare. The results have implications to theory and practice, indicating that adopting personal data management principles requires transformation of the business model, which, if successfully managed, may provide access to more resources, potential to offer better value, and additional customer channels. These advantages correlate with the broadening of the business ecosystem. Expanding the scope of this study to include more actors would improve the validity of the research. The results draw from existing literature and are based on findings from a case study and the economic properties of the healthcare industry in Finland.

Keywords: Ecosystem, business model, personal data, preventive healthcare.

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Authors: Pedro J. Muñoz Merino, Natividad Martínez Madrid, Ralf E. D. Seepold

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On one hand, SNMP (Simple Network Management Protocol) allows integrating different enterprise elements connected through Internet into a standardized remote management. On the other hand, as a consequence of the success of Intelligent Houses they can be connected through Internet now by means of a residential gateway according to a common standard called OSGi (Open Services Gateway initiative). Due to the specifics of OSGi Service Platforms and their dynamic nature, specific design criterions should be defined to implement SNMP Agents for OSGi in order to integrate them into the SNMP remote management. Based on the analysis of the relation between both standards (SNMP and OSGi), this paper shows how OSGi Service Platforms can be included into the SNMP management of a global enterprise, giving implementation details about an SNMP Agent solution and the definition of a new MIB (Management Information Base) for managing OSGi platforms that takes into account the specifics and dynamic nature of OSGi.

Keywords: MIB, OSGi, Remote Management, SNMP.

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