Can ChatGPT Predict Stock Market Price Movements? - Ammar Esrawi; Bassel Alkhatib (Journal of Digital Information Management, 2024)
Accelerated advances in network speed, re- liability, and security have increased the demand for soft- ware and services to move from being stored and pro- cessed locally on users’ devices to being managed by third parties that can be accessed through the network. This has led to the need to develop new software archi- tectures and software architectures that meet these new requirements. One example of software architecture is the recent emergence of a microservices architecture to meet online service providers’ maintenance and scalability requirements. Microservices is an architecture style that focuses on breaking down a system into small, lightweight services that are intentionally created to perform a highly cohesive business function, and it is an evolution of the traditional service-oriented architecture style. The practi- cal projection of microservices architecture in informa- tion engineering is several small applications that work together to provide an integrated service that appears to the external medium as a single application. Each of these applications has its own independent source code and deals with data sources that are different from the rest. As a result, it has an independent development team. Partial applications depend on each other to accomplish their functions, and therefore, any modification to one of the services will affect the work of the rest of the ser- vices; in large applications that require keeping the sys- tem always available, we must perform the process of publishing updates for parts of the system without stop- ping it completely, so we must monitor the impact of modi- fications and forecast system status before new updates are deployed. In achieving this prediction, we must know the dependencies between different services to arrive at a cognitive model representing the dependencies between services or applications.

Convolutional Neural Networks for Handwritten Text Recognition of Medical Prescription- - Makarand Shahade; Mayuri Kulkarni; Vivek Pawar; Jatin Chaudhari; Yash Lakade; Darshan Kotkar (Journal of Digital Information Management, 2023)
Converting handwritten prescriptions into electronic format offers several advantages and is crucial for modern healthcare systems. It is essential nowadays because of some factors such as – Legibility and Accuracy: Handwritten prescriptions can be challenging to read and interpret; accessibility and Portability: Electronic prescriptions can be easily stored; Decision Support Systems: By digitising prescriptions, healthcare systems can integrate them with electronic health records (EHRs) and utilise decision support systems. Convolutional neural networks (CNNs) are a class of deep learning algorithms that have proven effective in extracting handwritten text from various documents, including medical prescriptions. By leveraging CNNs for handwritten text extraction, healthcare systems can automate the process of digitising prescriptions, reducing manual effort and potential human errors. This enables seamless integration with electronic systems, facilitating better patient care and overall healthcare management. In this paper, we have trained the CNN model for different parameters and observed the accuracy and loss for various parameters. We got a maximum training accuracy of 89% and a maximum testing accuracy of 70%.

Convolutional Neural Networks for Handwritten Text Recognition of Medical Prescription- - Makarand Shahade; Mayuri Kulkarni; Vivek Pawar; Jatin Chaudhari; Yash Lakade; Darshan Kotkar (Journal of Digital Information Management, 2023)
<strong style="margin: 0px; padding: 0px; border: 0px; font-variant-numeric: inherit; font-variant-east-asian: inherit; font-variant-alternates: inherit; font-variant-position: inherit; font-stretch: inherit; font-size: 15.2015px; line-height: inherit; font-family: Raleway, Arial, Tahoma; font-optical-sizing: inherit; font-size-adjust: inherit; font-kerning: inherit; font-feature-settings: inherit; font-variation-settings: inherit; vertical-align: baseline; color: rgb(17, 17, 17); text-align: justify;"> Converting handwritten prescriptions into electronic format offers several advantages and is crucial for modern healthcare systems. It is essential nowadays because of some factors such as – Legibility and Accuracy: Handwritten prescriptions can be challenging to read and interpret; accessibility and Portability: Electronic prescriptions can be easily stored; Decision Support Systems: By digitising prescriptions, healthcare systems can integrate them with electronic health records (EHRs) and utilise decision support systems. Convolutional neural networks (CNNs) are a class of deep learning algorithms that have proven effective in extracting handwritten text from various documents, including medical prescriptions. By leveraging CNNs for handwritten text extraction, healthcare systems can automate the process of digitising prescriptions, reducing manual effort and potential human errors. This enables seamless integration with electronic systems, facilitating better patient care and overall healthcare management. In this paper, we have trained the CNN model for different parameters and observed the accuracy and loss for various parameters. We got a maximum training accuracy of 89% and a maximum testing accuracy of 70%.

COVID- 19 pandemic – An Empirical Study on the Cybersecurity Behaviour of Healthcare Sectors and Employees - Harrison Stewart (Journal of Digital Information Management, 2022)
Earlier research by our team have focussed the multilayer SAM spiking neural network using training algorithms for implementing FPGA. In the current work we have outlined the utilization of SAM-based network for developing function approximation. We have deployed the spike coding model for the work. In the testing we have proved that the â€œinterpolated XORâ€ and 3-polynominal function approximation of this SAM network. We found that the SAM network has the ability to perform these function approximations to high accuracy.

Comparison of the Effects Stemmer Porter and Nazief-Adriani on the Performance of Winnowing Algorithms for Measuring Plagiarism- - Alam Rahmatulloh; Neng Ika Kurniati; Irfan Darmawan; Adi Zaenal Asyikin; Deden Witarsyah (Journal of Digital Information Management, 2020)
Current technological developments change physical paper patterns into digital, which has a very high impact. Positive impact because paper waste is reduced, on the other hand, the rampant copying of digital data raises the amount of plagiarism that is increasing. At present, there are many efforts made by experts to overcome the problem of plagiarism, one of which is by utilizing the winnowing algorithm as a tool to detect plagiarism data. In its development, many optimizing winnowing algorithms used stemming techniques. The most widely used stemmer algorithms include stemmer porter and nazief-adriani. However, there has not been a discussion on the comparison of the effect of performance using stemmer on the winnowing algorithm in measuring the value of plagiarism. So it is necessary to do research on the effect of stemmer algorithms on winnowing algorithms so that the results of plagiarism detection are more optimal. The results of this study indicate that the effect of nazief-adriani stemmer on the winnowing algorithm is superior to the stemmer porter, only decreasing the detection performance of the 0.28% similarity value while the porter stemmer is superior in increasing the processing time to 69% faster.

A CBR Decision Support System for Loan Evaluation - Abdelkader Adla; Noureddine Benmessaoud (Journal of Digital Information Management, 2019)
Group decision making is time-consuming, complex, critical and recurring in nature. In group decision making, the decision-makers often perform periodic and repetitive activities. This prompts the decision makers to store their knowledge and their experience invoked in prior problem solving sessions. The development of this group knowledge clearly assists in similar later decision-making activities and reduces the time required to come to a decision, particularly, in a critical situation. This shared knowledge constitutes the group memory. In this paper, we develop a Case-Based Reasoning Decision Support System called CBR-DSS that makes use of the rules, policies and standard procedures of prior groups embedded in the group memory. The CBR process is evolved by using the Analytic Hierarchy Process (AHP). In an effort to enhance utility and show the feasibility, we experiment our approach in a bank loan analysis domain, a semi-structured multiple attributes decision-making environment, and compare CBR-DSS with a conventional case based reasoning system. CBR-DSS will compensate for lack of experience of young bank consultants, and allow disseminating and distributing available experience to different bank sites.
Keywords: Case-based Reasoning; Group Decision Making; Group Memory; Information retrieval; Recommender system

A CBR Decision Support System for Loan Evaluation- - Abdelkader Adla; Noureddine Benmessaoud (Journal of Digital Information Management, 2019)
Group decision making is time-consuming, complex, critical and recurring in nature. In group decision making, the decision-makers often perform periodic and repetitive activities. This prompts the decision makers to store their knowledge and their experience invoked in prior problem solving sessions. The development of this group knowledge clearly assists in similar later decision-making activities and reduces the time required to come to a decision, particularly, in a critical situation. This shared knowledge constitutes the group memory. In this paper, we develop a Case-Based Reasoning Decision Support System called CBR-DSS that makes use of the rules, policies and standard procedures of prior groups embedded in the group memory. The CBR process is evolved by using the Analytic Hierarchy Process (AHP). In an effort to enhance utility and show the feasibility, we experiment our approach in a bank loan analysis domain, a semi-structured multiple attributes decision-making environment, and compare CBR-DSS with a conventional case based reasoning system. CBR-DSS will compensate for lack of experience of young bank consultants, and allow disseminating and distributing available experience to different bank sites.

Computer and Network Security: Ontological and Multi-agent System for Intrusion Detection - Samir Bourekkache; Okba Kazar; Ahmed Aloui (Journal of Digital Information Management, 2019)

Computer and Network Security: Ontological and Multi-agent System for Intrusion Detection- - Samir Bourekkache; Okba Kazar; Ahmed Aloui (Journal of Digital Information Management, 2019)
Today the security aspect is a backbone of every computer system, particularly when this system contains sensitive information. When we use our computers and connect to the network or use web services, we trust that our personal and sensitive information (images, passwords, credit card number …etc) are confidential and well secured by the used security system. Unfortunately, the hackers may succeed to gain access to the system and misuse our information. Generally, the aim of the intrusion is to find new ways to annoy, steal and harm parts of computer systems. Moreover, with the possibility of connecting several computers and networks, the necessity of protecting the whole data and machines from attackers (hackers) that try to get some confident information to use for their own benefit or just destroy or modify valuable information was born. At this point IDS appears to help users, companies or institutions to detect when they are getting compromised. In this paper, we develop an intrusion detection system using multi agent system, ontological techniques (IDSMAO) and misuse method.

Corpus-Based Techniques for Sentiment Lexicon Generation: A Review- - Mohammad Darwich; Shahrul Azman Mohd Noah; Nazlia Omar; Nurul Aida Osman (Journal of Digital Information Management, 2019)
State-of-the-art sentiment analysis systems rely on a sentiment lexicon, which is the most essential feature that drives their performance. This resource is indispensable for, and greatly contributes to, sentiment analysis tasks. This is evident in the emergence of a large volume of research devoted to the development of automated sentiment lexicon generation algorithms. The task of tagging subjective words with a semantic orientation comprises two core approaches: dictionarybased and corpus-based. The former involves making use of an online dictionary to tag words, while the latter relies on co-occurrence statistics or syntactic patterns embedded in text corpora. The end result is a linguistic resource comprising a priori information about words, across the semantic dimension of sentiment. This paper provides a survey on the most prominent research works that utilize corpus-based techniques for sentiment lexicon generation. We also conduct a comparative analysis on the performance of state-of-the-art algorithms proposed for this task, and shed light on the current progress and challenges in this area.

Crawling the Dark Web: A Conceptual Perspective, Challenges and Implementation– - Bassel Alkhatib; Randa Basheer (Journal of Digital Information Management, 2019)
Internet and network technologies have evolved dramatically in the last two decades, with rising users’ demands to preserve their identities and privacy. Researchers have developed approaches to achieve users’ demands, where the biggest part of the internet has formed, the Deep Web. However, as the Deep Web provides the resort for many benign users who desire to preserve their privacy, it also became the perfect floor for hosting illicit activities, which generated the Dark Web. This leads to the necessity of finding automated solutions to support law and security agencies in collecting information from the Dark Web to disclose such activities. In this paper, we illustrate the concepts needed for the development of a crawler that collects information from a dark website. We start from discussing the three layers of the Internet, the characteristics of the hidden and private networks, and the technical features of Tor network. We also addressed the challenges facing the dark web crawler. Finally, we presented our experimental system that fetches data from a dark market. This approach helps in putting a single dark website under investigation, and can be a seed for future research and development.

Crawling the Dark Web: A Conceptual Perspective, Challenges and Implementation - Bassel Alkhatib; Randa Basheer (Journal of Digital Information Management, 2019)

Cloud Computing Intrusion Detection Using Artificial Bee Colony-BP Network Algorithm - Yang Hui (Journal of Digital Information Management, 2018)

Cloud Computing Intrusion Detection Using Artificial Bee Colony-BP Network Algorithm- - Yang Hui (Journal of Digital Information Management, 2018)
Cloud computing characterizes a methodology for computing communications in a much effective manner, and a business paradigm for trading computing resources and services. Alternatively, these difficult and distributed planning’s turn a striking objective for intruders. Cloud computing provides huge latent for enhancing production and decrease expenditures. However it simultaneously acquires several novel security risks. Intrusion Detection Systems (IDS) have been employed broadly for identifying malicious actions in network communication and hosts. In this work, an artificial bee colony-BP neural network algorithm is applied to the detection module, in order to detect the complicated aggressive behaviors. Through example verification, the artificial bee colony-BP network algorithm has improved intrusion detection efficiency and classification precision, and can effectively guarantee the safety of the cloud computing environment.

A Comparative Analysis of the Efficiency of Video Reader Object for Frame Extraction in MATLAB - M Nachamai; Joy Paulose; Samuel Marandi (Journal of Multimedia Processing and Technologies, 2018)

Content Based Text Information Search and Retrieval in Document Images for Digital Library- - A. Sakila; S. Vijayarani (Journal of Digital Information Management, 2018)
The main objective of this research work is to find the keywords in the captured/scanned print document images in the image database. Document images are becoming more popular in today’s world and these are used in paperless offices and digital libraries. Information retrieval from the document images is a very challenging task. Hence, there is a need for developing searching strategies to find the required information from these document images as per user’s needs, becomes very essential in nowadays. Traditionally Optical Character Recognition (OCR) tools are used for information retrieval from the document images, but it’s not an efficient method. Word spotting is an inventive method for searching the document images and to retrieve relevant information without any conversion. In this work an algorithm Enhanced Dynamic Time Warping was proposed to for finding keywords from document images, it is based on word spotting technique.

Content Based Text Information Search and Retrieval in Document Images for Digital Library - A. Sakila; S. Vijayarani (Journal of Digital Information Management, 2018)

A Cross-Layered Approach for Improving Quality of Service of Video Transmission in Ad-hoc Networks - Mohammed Yasin Ali; Syed Musthak Ahmed (Journal of Networking Technology, 2018)

Comparing network centrality measures as tools for identifying key concepts in complex networks: A case of wikipedia - Matas, N.; Martincić-Ipšić, S.; Meštrović, A. (Journal of Digital Information Management, 2017)

Computer aided fire safety engineering model based on shortest path algorithm - Gao, Y.; Zhao, J. (Journal of Digital Information Management, 2017)

A cloud-assisted architecture for content distribution in mobile peer to peer networks - Liu, Y.-N.; Chen, H.-Y.; Gilani, S.M.M. (Journal of Digital Information Management, 2016)

A clustering with slope algorithm based on mapreduce - Wang, Y.; Wu, H.; Liang, J.; Ou, J.; Huang, Y. (Journal of Digital Information Management, 2016)

A combination indexing for image social bookmarking system to improve search results - Jomsri, P. (Journal of Digital Information Management, 2016)

Construction of trust based dynamic access control model in P2P network environment - Han, Y.; Cui, X. (Journal of Digital Information Management, 2016)

Cloud-based government procurement information integration platform - Wang, P.Y.; Shen, J.; Guo, W.Q.; Zhang, C.; Zhang, B. (Journal of Digital Information Management, 2015)

Clustering research across Tibetan and Chinese texts - Xu, G.X.; Sun, W.; Peng, X.P. (Journal of Digital Information Management, 2015)

Collection and selection based relevant degrees of documents - Kheira, M.; Lougmiri, Z.; Kamel, A.M. (Journal of Digital Information Management, 2015)

Computer graphics algorithm based on visualization teaching theory - Wang, F. (Journal of Digital Information Management, 2015)

Construction and application of teaching file management system in institutions of higher learning based on OpenKM - Deng, H. (Journal of Digital Information Management, 2015)

Construction of a Port Logistics Information Platform Based on J2EE: Taking Dushan Port of Jiaxing as an Example - Shen, Y.Y.; He, A.T. (Journal of Digital Information Management, 2015)

Cooperation based on decode-and-forward plus cooperative jamming for wireless physical layer security - Huang, S.; Tan, J.; Jiang, R. (Journal of Digital Information Management, 2015)

Campsna: A cloud assisted mobile peer to peer social network architecture - Liu, Y.-N.; Tang, H.; Zhao, G.-F. (Journal of Digital Information Management, 2014)
In the social network, people with same interest construct a group, where people can share their interesting contents. With the popularity of the mobile devices, mobile users can connect to the internet for services such as downloading videos, music and pictures anywhere at any time, which may cause heavy traffic burden on the core network. The service content (eg. video, picture, music) downloaded and shared in the same social network interest group may always be similar, which may lead to repeated traffic transmitted in a short time period; on the other hand, the size of the service content may be very large for a single user, which will take it along time and spend much money to finish a complete service request or sharing. It is necessary to study how to reduce the requests in internet from the mobile users to reduce the flow on the core network, as well as reducing the economic cost and the service time, which is the main motivation of our research. Based on the universal mobile social network architecture, this paper proposed CAMPSNA: a cloud assisted mobile peer to peer social network architecture. In which, the cloud center is introduced to help the mobile devices offloading complex tasks to the cloud. The end devices in the architecture construct a local mobile peer to peer network to share their existing content based on their social relationships to reduce the burden of the cloud center.
Keywords: Data exchange; Mobile Cloud; Mobile peer to peer Network; Network Architecture; Social Network

The computer design of practical filter - Li, Z.; Weixi, Z. (Journal of Digital Information Management, 2013)
This paper discussed how to use computer to directly transfer the simulative low pass prototype design to all kinds of needed principles and design methods. This method's advantage is that it is very direct, and universal. So, this method is simple, convenient, practical and fast.
Keywords: Filter; Polynomial shift; Transform matrix

A clustering based forecast engine for retail sales - Murlidhar, V.; Menezes, B.; Sathe, M.; Murlidhar, G. (Journal of Digital Information Management, 2012)
Efficient and accurate sales forecasting is a vital part of creating an efficient supply chain in enterprises. Times series methods are a popular choice for forecasting demand sales. A major challenge is to develop a relatively inexpensive and automated forecasting engine that guarantees a desired forecasting accuracy. Times series decomposition and Forecast combination have been two classes of methods that have attracted the interest of recent researchers. One solution has been to use decomposition followed by recombining to form a very large number of forecasting models Further many recent papers present Data Mining based methods to intelligently discover a subset of methods from a large list that can be used in combination for sales demand forecasting. These methods are computationally expensive and prohibitive if they are applied to each individual time series in a retail organization. In this paper we present a novel technique to identify similar sales series and efficiently use the best combination of methods learnt for one series to forecast for the entire set of similar series.
Keywords: Bad experts; Combining; Decomposition; Frequent pattern mining; Good experts; Hierarchical clustering; Sales forecasting; Times series

CMCCR: Classification based on multiple class-correlation rules - Zhou, Z.; Wang, X.; Pan, G. (Journal of Digital Information Management, 2012)
Previous studies show that associative classification achieves higher classification accuracy than traditional classification approaches. However, associative classification suffers from two major deficiencies and thus sometimes has lower classification accuracy. First, it often generates a large number of rules when the minimum support is set to be low. It is not only time consuming to discover so many itemsets, but also very difficult to select high quality rules from a huge set of rules. Second, the association measure confidence does not reflect correlation relationships between itemsets and the class label. To deal these problems, in this paper, we propose a new classification approach called classification based on multiple class-correlation rules (CMCCR). First, we use both support and all-confidence to mine not only frequent but also mutually associated itemsets. This method not only saves the running time but as well considerably reduces the number of itemsets generated, and thus sharply decreases the number of class-correlation rules produced. Second, we use a new correlation measure correlation confidence to discover class-correlation rules. The measure correlation confidence has two bounds:- 1and 1. It is easy to control the correlation degree of the class-correlation rules found. Finally, we use multiple class-correlation rules and average correlation degree to measure the combined effect of group rules. Experimental results on the mushroom data set show that CMCCR has higher and more stable accuracy than associative classification and decision tree method.
Keywords: All-confidence; Class-correlation rule; Classification; Correlation confidence

A comparison of pattern classification approaches for structural damage identification - Qingqing, Z.; Qifeng, Z.; Yongpeng, N.; Fan, Y.; Jiayan, L.; Run, T.; Minda, L. (Journal of Digital Information Management, 2012)
A structural damage identification approach based on wavelet packet decomposition (WPD) and random forests (RF) was proposed and compared with other pattern classification approachs. The main procedure involves extracting energy features from vibration acceleration data through wavelet packet decomposition and then using these features as input for a RF classifier. The experiment was carried on an 8-storey steel shear building model in the case verification. The results show that the proposed method is effective for structure damage detection and can achieve a higher accuracy than other commonly used methods.
Keywords: Extraction algorithms; Pattern matching; Sensor identificaiton; Wavelet packet decomposition

A constrained optimization evolutionary algorithm based on membrane computing - Chen, H.; Lu, J. (Journal of Digital Information Management, 2012)
Based on membrane computing (MCCOP for short), a constrained optimization evolutionary algorithm is proposed with the evolutionary operations and strategies designed. In MCCOP, a membrane is associated with a constraint and the tentative solutions evolved according to the rules in the membrane. And it is evaluated by the constraint function value as the fitness. The subpopulations can communicate efficiently during the evolution process by making use of the structure of P systems and the communication mechanism among the membranes. The computational experiments show that MCCOP can converge to optimal or close to optimal solutions efficiently and that MCCOP outperforms or performs similarly to the other techniques referred to in terms of the quality of the resulting solutions.
Keywords: Constrained optimization; Membrane computing; Optimization algorithm; P system

Creating multi-level class hierarchy for question classification with NP analysis and wordnet - Bakhtyar, M.; Kawtrakul, A.; Baber, J.; Doudpota, S.M. (Journal of Digital Information Management, 2012)
Question answering systems provide answer to the questions using various question processing techniques and extracts answers from a set of documents. Finding the exact answer is more interesting and useful than getting a list of documents to look through and find the answer manually. Question answering is not same as a traditional document retrieval search engine where a set of relevant documents are returned in response of the query, whereas, in the question answering systems the response of the query is a concise and exact answer to the question. Typically, Question Classification (QC) is the first step in a Question Answering (QA) system. This phase is responsible for finding out the type of the expected answer by pruning out the extra information that is not relevant to extract the answer. Almost all the previous QC algorithms evaluated their work on the basis of a common class hierarchy already defined. The coarse grained classes Location, Entity and Numeric in the existing hierarchy have a fine grained class Other. We present the framework to create new fine grained classes to replace the Other classes. We also discuss the motivation behind the replacement and how the new fine grained classes may support the answer extraction. Additionally, we also present an automatic hierarchy creation method to add new class nodes using WordNet and Noun phrase parsing.
Keywords: Meta Information Management; Natural Language Processing; Question Answering4; Question Class Hierarchy; Question Classification

A combined measure of semantic web and meta tag technology for web retrieval - Al Reyaee, S. (Journal of Digital Information Management, 2011)
Increasingly web is used for tracking numerous information pieces of large and complex data. Normal way of accessing these sources using web search engines has many limitations and we have enough literature to document them. This study is an enhancement to trap the discovery of targeted information pieces which are normally failed cases in search engines. We use an integrated approach of conventional web search and semantic web by carefully constructed algorithms. The algorithms are tested to track data about the gulf institutions. The experiments provide encouraging results and enable us to draw future directions of research in this domain.
Keywords: Gulf institutions; Institutions profiles; Semantic web; Web construct data

Construction of a teaching resources sharing platform based on campus network - Tian, W. (Journal of Digital Information Management, 2011)
This paper constructs a teaching resources sharing platform based on campus network, which has integrated the existing digital teaching resources, and collected the scattered resources together and improved the whole unreasonable resources layout, repetitive construction and so on. Since the network is convenient and real-time, it is suited to be applied to the traditional classroom teaching. The teaching resources share platform can not only help students to make full use of information technology tools for the realization of personalized learning, but also can strengthen the learning interests and enthusiasms of the students.
Keywords: Campus Network; Sharing Platform; Teaching Resources

Case studies on context-aware mobile multimedia services - Ojala, T. (Journal of Digital Information Management, 2010)
This paper explores the design, implementation and evaluation of context-aware mobile multimedia services by presenting six case studies on different application domains. The case studies highlight the opportunities that mobile devices equipped with wireless data transmission provide for contextaware and ubiquitous computing. Special emphasis is placed on empirical evaluation of the services in the field in form of a user evaluation with genuine end users in real environment of use. The paper concludes with a discussion on some important lessons learned from the case studies.
Keywords: Context-aware system; GPRS network; Mobile advertising; Mobile multimedia; SmartRotuaari; WLAN

Cellular DBMS: An attempt towards biologically-inspired data management - Ur Rahman, S.S.; Köppen, V.; Saake, G. (Journal of Digital Information Management, 2010)
Existing database management systems (DBMS) are complex and less predictable (i.e., the consistency of performance with the increase of functionality and the data growth is not certain). Database researchers acknowledge the need for revisiting DBMS architectures to fulfill the needs of new hardware and application trends. We propose a biologically inspired DBMS architecture called "Cellular DBMS". The Cellular DBMS architecture promises development of highly customizable and autonomous DBMS. This paper explains in detail the design principles for Cellular DBMS architecture. It also explains an aspect-oriented programming based model to equip Cellular DBMS architecture with autonomy. Finally, it presents an extension to decomposed storage model (DSM) for use in Cellular DBMS.
Keywords: Adaptability; Biological-inspiration; Customization; Data management; DBMS architectures; Embedded databases

Content-based image retrieval systems-reviewing and benchmarking - Kosch, H.; Maier, P. (Journal of Digital Information Management, 2010)
The last detailed review of Content-based Image Retrieval Systems (CBIRS) is that from Veltkamp and Tanase [VT02], updated in 2002. Since then, many new systems emerged, other systems were improved, but many systems are no longer supported. This paper reconsiders the systems described by Veltkamp and Tanase and proposes in addition for a selection of existing CBIRS a quantitative comparison. For this purpose we developed a benchmarking system for CBIRS based on how accurate they could match ideal (ground truth) results. As measure we introduced the RankWRN , which we developed based on the Normalized Avarage Rank. Our measure allows fair and accurate comparisons (due to multiple similarity values) of different CBIRS with respect to different queries. None of the other benchmarks allow this comparison. The choice for a system to be compared quantitatively was motivated by availability of the source or runtime code and the liveness (i.e., active development on the CBIRS). The benchmarked systems were SIMBA, SIMPLIcity, PictureFinder, ImageFinder, Caliph&, VIPER/GIFT and Oracle Intermedia. Results show that VIPER/GIFT performs best in the settings of our benchmark, Caliph& is second and SIMBA third. The default parameter settings of the systems tend to show the best results. MPEG-7 descriptors, which are used in Caliph&, show a good performance in the benchmarking.
Keywords: Image ranking; MPEG-7 descriptors

Cache Pre-fetching and Replacement strategies for location dependent data in mobile environments - Mary Magdalene Jane, F.; Ilayaraja, N.; Nadarajan, R. (Journal of Digital Information Management, 2009)
In this paper, new strategies for prefetching and cache replacement are proposed. The proposed pre-fetching algorithm considers the geographical and semantic adjacency between queried items. The strategy is to pre-fetch the most probable secondary service as a by-product of the execution of a query to a primary service. Association rule mining is used for determining the secondary item that should be pre-fetched. The latter part of the paper deals with a cache replacement algorithm PAID+ (Probability Area Inverse Distance +) which takes into account the access probability and distance between the mobile client and the service item which suits a pre-fetching environment. The experimental evaluations using synthetic datasets show the cache pre-fetch and replacement policy PAID+ are effective in improving the system performance in terms of the cache hit ratio of mobile clients.
Keywords: Cache replacement; Location dependent data; Prefetching algorithm

CUDL language semantics: Conditions - Karanikolas, N.N. (Journal of Digital Information Management, 2009)
Conceptual Universal Database Language (CUDL) is a new language designed to manage dynamic database environments, which conform to the Frame DataBase model (FDB). FDB is a generic database model (oversubscribe both the Entity-Attribute-Value and the Nested Relational). CUDL is not only an FDB database language but it is mainly an agent that provides an abstraction level superior to the logical level. CUDL permits the users to conceive a database schema where single fields (tags) accept repetitions (list of values), entertain subfields and also permit to entertain an entire table in the place of a single field. In this paper we investigate the conditions, used in both retrieval and update CUDL statements, and focalize especially on the range of affection of the update statements. This becomes necessary since the CUDL abstraction level reveals the differentiation between frame objects, tag repetitions and subfield repetitions and consequently the update statements should clearly define where the data modifications occur. This need becomes heavier where some frame object verifies two (or more) disjunctive conditions and these conditions specify different range of affection inside the frame. We provide two solutions where the second one is an improvement of the first one but it demands more refined statements and more instructed / skilled users. Some constructive discussion is also provided.
Keywords: Conditions; Database languages; Dynamically evolving database environments

Citation auctions as a method to improve selection of scientific papers - De La Rosa, J.L.; Szymanski, B.K. (Journal of Digital Information Management, 2008)
This paper describes the basis of citation auctions that are introduced as a new approach to selecting scientific papers for publication. Unlike the state of the art in paper selection that relies on peer reviews, our main idea is to use an auction in which bids consist of the number of citations that an author expects to receive if the paper is published. Hence, a citation auction is the selection process itself, and no reviewers are involved. The benefits of the proposed approach are two-fold. First, the cost of refereeing will be either totally eliminated or significantly reduced, because the process of citation auction does not need prior understanding of the paper's content to judge the quality of its contribution. Additionally, the method does not prejudge the content of the paper, so it will increase the openness of publications to new ideas. Second, scientists will be much more committed to the quality of their submitted papers, paying close attention to distributing and explaining their papers in detail to maximize the number of citations that the paper receives when published. Sample analyses of the number of citations collected via Google scholar for papers published in years 1999-2004 in a series of proceedings, and in years 2003-2005 in a series of conferences (in a totally different discipline) are provided. Finally, a simple simulation of an auction is given to outline the behaviour of the citation auction approach.

Classification of digital libraries - An e-business model-based approach - Markscheffel, B.; Fischer, D.; Stelzer, D. (Journal of Digital Information Management, 2008)
The impact of digital libraries on information processes steadily increases. Today, users may choose from a rapidly increasing number of digital libraries with various characteristics. Business models for digital libraries evolve from mere content providers to sophisticated service suppliers They offer a rich variety of information services combined with collaboration, e-learning and portal features. Traditional classification schemes for digital libraries do not sufficiently take this development into account or fail to meet it at all. In this paper we present a novel approach to the classification of digital libraries. It is based on business model characteristics with special emphasis on the evolution of digital libraries from content to information service providers. Our classification approach helps suppliers to communicate benefits and features of their libraries more effectively. It also supports customers when selecting an appropriate digital library.
Keywords: Digital libraries; Digital libraries evaluation; Information processing

Classification of geographic web services for building an efficient geographic information system offering profitable value added services - Kleiner, C.; Aliste, A.G. (Journal of Digital Information Management, 2008)
In this paper we present best practices in implementing a geographic information system that may be used as a foundation for commercial geographic web services. On one hand we explain important rules to be followed during the design of the information system itself, e.g. the data supplyment process. On the other hand we introduce different categories for potential geographic web services and analyze these categories according to several properties. From some of those properties rules can be extracted on how well every kind of service is suited for commercial use and which technical context (e.g. security) has to be observed to be able to offer it. The whole article is based on a case study and prototype where we implemented a geographic database and geographic web services from each of the different categories in the domain of hydrology.
Keywords: Case study; Commercial web service; Geo web service; Geographic services; GIS; Hydrology; Web service

Cluster based mixed coding schemes for inverted file index compression - Chen, J.; Zhong, P.; Cook, T. (Journal of Digital Information Management, 2008)
The cluster property of document collections in today's search engines provides valuable information for index compression. By clustering d-gaps of an inverted list based on a threshold, and then encoding clustered and non-clustered d-gaps using different methods, we can tailor to the specific properties of different d-gaps and achieve better compression ratio. Based on this idea, in this paper we propose a cluster based approach and presents two new codes for inverted file index compression: mixed gamma/flat binary code and mixed delta/flat binary code, Experiment results show that the two new codes achieve better or equal performance in terms of compression ratio comparing to interpolative code which is considered as the most efficient bitwise code at present Besides, the two new codes have much lower complexity comparing to interpolative code and therefore enable faster encoding and decoding. By adjusting the parameters for the mixed codes, even better results may be achieved. Experiments show promising results with our approaches.
Keywords: d-gap; Index compression; Inverted file; Inverted list

Commius - An email based interoperability solution tailored for SMEs - Burkhart, T.; Werth, D.; Loos, P. (Journal of Digital Information Management, 2008)
More than 99% of European enterprises are SMEs (Small and Medium-sized Enterprises). While in the current market situation collaboration is evolving to an indispensable key factor of success, research is yet to produce results which can be used by SMEs. This paper introduces an interoperability approach in the context of the European Commius Project which enables SMEs collaborate easily on the level of process interoperability. The Commius solution will dock onto existing E-Mail structures and enrich incoming E-Mails with additional information dependant upon its content and provide recommendations of expedient further steps to process the E-Mail. A first picture of the Commius process interoperability layer architecture will be drawn. Furthermore proposals to adapt Commius to the specific processes every SME has, in a simple way, are being exposed.
Keywords: Business process; Collaboration; Email; Interoperability; SME

Complex query processing on Web graph: A social network perspective - Mitra, S.; Bagchi, A.; Bandyopadhyay, A.K. (Journal of Digital Information Management, 2008)
A social network represents a social community as a directed graph. Communication on the Web has given rise to social network formation like, Web Community, Referral System etc. An earlier effort has proposed a data model for such Web-based social network. Present paper discusses the relevant index structures for processing queries on a social network schema based upon the proposed data model. The paper has also provided evaluation of the structural operators proposed in the data model and discussed their efficacy with query examples.
Keywords: Operator Evaluation; Query processing; Social Network; Web Graph; XB-tree

Co-training for search-based automatic image annotation - Zhao, Y.; Zhao, Y.; Zhu, Z. (Journal of Digital Information Management, 2008)
Search-based automatic image annotation is an effective technology to enhance the performance of annotation. By integrating the co-training technique, this paper addresses on a novel scheme for search-based image annotation, in which two classifiers can contribute to each other during the training phase. Since each classifier can select some most confident images to enhance the generalization ability of the other one, the co-training learning algorithm is triggered out for automatically mining more and more relevant images, which improve the annotation performance greatly. To characterize the various contribution of each relevant image, the probability output of the classification is taken as the corresponding weight. Moreover, the histogram of retrieved keywords is proposed to re-rank the final reliability of the keywords to be annotated, which can also guarantee the scalability property of annotation to some extent. With the promoted search precision on the basis of co-training strategy, the experimental results demonstrate the improvement of annotation performance.
Keywords: Automatic image annotation; Bayesian classifier; Co-training; SVM

Clustering frequent graph patterns - Liu, Y.; Li, J.; Zhu, J.; Gao, H. (Journal of Digital Information Management, 2007)
In recent years, graph mining has attracted much attention in the data mining community. Several efficient frequent subgraph mining algorithms have been recently proposed. However, the number of frequent graph patterns generated by these graph mining algorithms may be too large to be effectively explored by users, especially when the support threshold is low. In this paper, we propose to summarize frequent graph patterns by a much smaller number of representative graph patterns. Several novel concepts such as δ-cover graph, jump value and δ-jump pattern are proposed for efficiently summarizing frequent graph patterns. Based on the property of all δ-jump patterns being representative graph patterns, we propose two efficient algorithms for summarizing frequent graph patterns, RP-FP and RP-GD. The RP-FP algorithm computes representative graph patterns from a set of closed frequent graph patterns, whereas the RP-GD algorithm directly mines representative graph patterns from graph databases. The RP-FP has tight approximation (summarization) bound but higher computational complexity. The RP-GD has no approximation bound guarantee but is far more efficient. We conducted extensive experimental studies using various real and synthetic datasels. Experimental results show that RP-FP and RP-GD are able to obtain compact summarization in both real and synthetic graph databases. When the number of closed graph patterns is very large, RP-GD is much more efficient than RP-FP, while achieving comparable summarization quality.
Keywords: Closed graph pattern; Frequent graph pattern; Graph mining; Representative graph pattern; δ-jump pattern

A comparative study on key phrase extraction methods in automatic Web Site Summarization - Zhang, Y.; Milios, E.; Zincir-Heywood, N. (Journal of Digital Information Management, 2007)
Web Site Summarization is the process of automatically generating a concise and informative summary for a given Web site. It has gained more and more attention in recent years as effective summarization could lead to enhanced Web information retrieval systems such as searching for Web sites. Extraction-based approaches to Web site summarization rely on the extraction of the most significant sentences from the target Web site based on the density of a list of key phrases that best describe the entire Web site. In this work, we benchmark five alternative key phrase extraction methods, TFIDF, KEA, Keyword, Keyterm, and Mixture, in an automatic Web site summarization framework we previously developed. We investigate the performance of these underlying methods via a formal user study and demonstrate that Keyterm is the best choice for key phrase extraction while Mixture should be used to obtain key sentences. We also discuss why one method performs better than another and what could be done to further improve the summarization system.
Keywords: Key phrase extraction; Web retrieval; Web site study

Contextualizing multimedia semantics towards personalised eLearning - Eze, E.; Ishaya, T.; Wood, D.M. (Journal of Digital Information Management, 2007)
Recent developments of the Internet and the World Wide Web (WWW) have resulted in the proliferation of multi-media, resources and learning with existing multimedia web resources on the Web is becoming more prevalent and important. While recent standardization efforts in eLearning such as LOM, SCORM, and IMS Learning Design work towards learning content description, packaging, and delivery; existing eLearning solutions still lack the ability to adequately use multi-media resources to provide a learner with personalised learning resources. Effective use of multimedia for web-based learning provides quality interactive learning experience, but current techniques does not adequately provide a semantic approach for organising multimedia resources. With evolving trend in learning through the use of web technology, eLearning systems are expected to provide personalised learning resources for effective learning. We have accordingly proposed a way of organising existing multimedia resources based on contexts towards providing a truly personalised eLearning experience. This paper presents a framework for achieving context-based multimedia semantic annotation and organisation towards personalised learning.
Keywords: Multimedia technology; Web based learning; WWW

Continuously evaluating approximate similarity search on high dimensional data stream - Wang, W.; Li, J.; Ai, C.; Shi, S. (Journal of Digital Information Management, 2006)
In many applications, including online video monitoring system, it is often necessary to find out the most similar sequence, from a sequence set in database, for the high dimensional data stream. Due to its high computation complexity and the restrictions of query processing on data stream, continuously retrieving the similar content on high dimensional data stream is very challenging. The evaluating algorithm is expected to be very fast to match data stream arrival rate. To address this problem, we propose a method called CVNN in this paper. In our method, the sequences in database are represented into the compact summaries, namely CVs, which can be stored in memory. An online algorithm is conducted to transform data stream into a small number of CVs continuously, at the same time the nearest neighbor query is processed periodically based on the similarity of CVs. Experimental results show that our method is fairly effective.
Keywords: Data stream; High dimensional space; Query processing; Similarity Search; Video indexing

Creating dependable web services using user-transparent replica - Hillenbrand, M.; Götze, J.; Müller, P. (Journal of Digital Information Management, 2006)
Dependability is a major concern in software development, deployment, and operation. A commonly accepted solution for providing fault tolerant services on the Internet is to create replica of the services and to deploy them to several hosts. Whenever the service or the underlying node or network fails, another service is ready to take over. In the Venice project, several techniques are combined to create a dependable framework for deploying and managing distributed services using replica on several distinct network nodes.
Keywords: Dependable web services; Software development; Venice framework; Web service technology; XML