User Transactions Research Articles

Scalable Fast Scheduling for Optical Flow Switching Using Sampled Entropy and Mutual Information Broadcast

Optical flow switching is a promising architecture to service large transactions of end users with cost-effective and power-efficient direct access to all-optical networks. For dynamic sessions that are bursty, unscheduled, and only require short transmission times at the full rate of a single wavelength (approximately ones to tens of seconds), the network management and control effort can be substantial, even unimplementable if fast booking and initiation of flow service is needed. In this paper, we describe a fast scheduling algorithm that sets up end-to-end connections for users with urgent large transactions with a scheduling delay of slightly more than one round-trip time. This fast setup of connections is achieved by probing multiple lightpaths between the source and the destination. Probing multiple lightpaths is necessary for moderate to high network loads to achieve low blocking probability. However, the network burden of network-state updates and computational complexity of scheduling can be overwhelming and make the algorithm hard to scale to large networks. With the help of information about network regions periodically updated in the form of sampled entropy and mutual information of the network states, the required efforts can be substantially reduced. To minimize probing efforts and avoid unnecessarily tying up network resources, we use a modified Bellman — Ford Algorithm (Entropy — BF) to select the fewest lightpaths for probing that can satisfy a service-level-blocking probability agreement between the user and the network provider. By collapsing detailsof network states into scalar parameters for average entropy and mutual information, we can greatly reduce both the amount of network-state information collected and/or disseminated and the computation complexity of the lightpath selection process of the probing algorithm. The algorithm is also robust to variations of traffic statistics because it does not depend on detailed assumptions about the statistical model of the traffic, which is often unknown and highly variable in real networks. The throughput performance of this access protocol can be kept high while the network-state protocol burden and computation efforts are reduced by orders of magnitude.

Adapted Web Crawler for Mining Offline Web Data using AFHC

focused hyperlink crawler (AFHC) aim to search the entire inner level sub link of the web pages related to a specific topic and to download unique web pages to local disk. The download web page information searched in offline browsing and avoids the repeated searches in the web server to give a solution to problem. The major problem is to retrieve the maximal set of relevant and quality pages. Crawler software can be retrieve web pages by hyperlinks through internet. The number of web sites using web crawlers cannot retrieve the relevant pages. The system have browser and search engine. Browser using AFHC reduce time to finding accurate content in web pages in the hyperlinks and also restrict to download web pages to local disk. Search engine using extended cocitaion algorithm to retrieve accurate content in the local disk and search based on any word, all word and phrase matching in the local disk. It is useful to the student and organization. The research is easily found to personalize the crawl history and search history for knowing the user transactions.

Pass Fail

This article reviews different research works undertaken to construct user-friendly passwords for websites, which in turn result in smooth user transaction. Ryu and Moshfeghian have reviewed 96 websites within 16 categories, and then have narrowed their study to the top six sites within the dating and travel categories, as selected by Google. The findings are useful to users who type a password into a website and to the companies that want to offer smooth user transaction. The study shows that an effective password registration may produce a higher number of successfully registered accounts, which can translate to increased sales, brand status, and recognition. Some websites call upon picture identification for partial authentication along with the password. However, fancier password recognition software applications that would recognize faces or fingerprints or verbal inflection are further down the line.

Capturing Mobile User Behaviors: A Network Measurement Approach

Understanding the online user behaviors in the mobile app markets is vital for the development of mobile app ecosystems. However, user transaction data is difficult to obtain by the general research community so that little efforts have been paid to measure such behaviors. Therefore, we develop novel approaches to characterize the user behaviors. We firstly exploit app networks from the app market, which are derived from user behaviors thus capture the behavior features. We then introduce the metrics of network measurement and propose a new method to measure the app similarity. Using these metrics and methods, we generate statistics of the app networks and reveal patterns and motivations of user behaviors, which may give fundamental data and motivation supports for future works.

Solitude Conserve Attribute Cryptographic CP-ABFE Data Protocols in Fuzzy Cloud Service Provider

Background/Objectives: Privacy is considered as non-compromising demand of the user in utilities where the user’s private and vital information such as user name, user transaction details, user ID, and other specific particulars are used. In this situation the application of conservative cryptographic schemes and unspecified authentications are not enough, and so the implementation of special kind Cipher Text-Policy Attribute-Based Fuzzy Encryption (CP-ABFE) is introduced. It ensures authentication, integrity of communications, data security and user privacy to the consumers on cloud. Methods/ Statistical Analysis: We developed a model known as CP-ABFE for providing security for CP-ABE. We made analysis with CP-ABFE scheme. The achieved results show that our module reached with more tedious access controls and hierarchical attributes in Fuzzy Cloud Service Provider. Results: The achieved results are used to validate our proposed model and identified that the solution is very efficient and effective in supporting fuzzy encrypted data search over Fuzzy CSP data. Conclusion/Application: In this system, a user’s private key is associated with an arbitrary number of attributes which uttered as a string. On the other, if a party encrypts a message in our system, then that states an associated access construction over the attributes.

A survey of B-tree logging and recovery techniques

B-trees have been ubiquitous in database management systems for several decades, and they serve in many other storage systems as well. Their basic structure and their basic operations are well understood including search, insertion, and deletion. However, implementation of transactional guarantees such as all-or-nothing failure atomicity and durability in spite of media and system failures seems to be difficult. High-performance techniques such as pseudo-deleted records, allocation-only logging, and transaction processing during crash recovery are widely used in commercial B-tree implementations but not widely understood. This survey collects many of these techniques as a reference for students, researchers, system architects, and software developers. Central in this discussion are physical data independence, separation of logical database contents and physical representation, and the concepts of user transactions and system transactions. Many of the techniques discussed are applicable beyond B-trees.

Recommendations by Randomly Walking on Cross Domain User Graph

Content providers want to make recommendations across multiple interrelated domains such as music and movies. However, existing collaborative filtering methods fail to accurately identify items that may be interesting to the user but that lie in domains that the user has not accessed before. This is mainly because of the paucity of user transactions across multiple item domains. Our method is based on the observation that users who share similar items or who share social connections, can provide recommendation chains (sequences of transitively associated edges) to items in other domains. It first builds domain-specific-user graphs (DSUGs) whose nodes, users, are linked by weighted edges that reflect user similarity. It then connects the DSUGs via the users who rated items in several domains or via the users who share social connections, to create a cross-domain- user graph (CDUG). It performs Random Walk with Restarts on the CDUG to extract user nodes that are related to the starting user node on the CDUG even though they are not present in the DSUG of the starting user node. It then adds items possessed by those users to the recommendations of the starting node user. Furthermore, to ex- tract many more user nodes, we employ a taxonomy-based similarity measure that states that users are similar if they share the same items and/or same classes. Thus we can set many suitable routes from the starting user node to other user nodes in the CDUG. An evaluation using rating datasets in two interrelated domains and social connec- tion histories of users as extracted from a blog portal, indicates that our method identifies potentially interesting items in other domains with higher accuracy than is possible with existing CF methods.

Man in the Browser Attacks

Man-in-the-Browser attacks are a sophisticated new hacking technique associated with Internet crime, especially that which targets customers of Internet banking. The security community has been aware of them as such for time but they have grown in ability and success during that time. These attacks are a specialised version of Man-in-the-Middle attack, and operate by stealing authentication data and altering legitimate user transactions to benefit the attackers. This paper examines what Man-in-the-Browser attacks are capable of and how specific versions of the attack are executed, with reference to their control structure, data interaction techniques, and methods for circumventing security. Finally the authors discuss the effectiveness of counter-Man-in-the-Middle strategies, and speculate upon what these attacks tell us about the Internet environment.

Dynamic Damage Recovery for Web Databases

In the web context, there is an urgent need for a self-healing database system which has the ability to automatically locate and undo a set of transactions that are corrupted by malicious attacks. The metrics of survivability and availability require a database to provide continuous services during the period of recovery, which is referred to as dynamic recovery. In this paper, we present that an extended read operation from a corrupted data would cause damage spreading. We build a fine grained transaction log to record the extended read and write operations while user transactions are processing. Based on that, we propose a dynamic recovery system to implement the damage repair. The system captures damage spreading caused by extended read-write dependency between transactions. It also retains the execution results for blind write transactions and gives a solution to the issues of recovery conflicts caused by forward recovery. Moreover, a confinement activity is imposed on the in-repairing data to prevent a further damage propagation while the data recovery is processing. The performance evaluation in our experiments shows that the system is reliable and highly efficient.

New user transaction algorithm

This study proposed a user business algorithm with double thresholds.This algorithm first acted according to the page number which the user visited to judge whether this user was the accidental user,and then the network topology and homepage lowest interest degree to judge whether the homepage appealed to the users.This method improved the data pretreatment process,and deleted the visit record which the accidental user caused,as well as the link pages and the pages that users were not interested in,produced one kind of effective visiting page sequence,namely double thresholds user business.This paper has proved the validity of the algorithm through an instance.

Performance problem prediction in transaction-based e-business systems

Key areas in managing e-commerce systems are problem prediction, root cause analysis, and automated problem remediation. Anticipating SLO violations by proactive problem determination (PD) is particularly important since it can significantly lower the business impact of application performance problems. The main contribution of this paper is to investigate proactive PD based on two important concepts: dependency graphs and dynamic runtime performance characteristics of resources that comprise an I/T environment. The authors show how one can calculate and use the contribution of all supporting resources for a transaction to the end-to-end SLO for that transaction. Higher order moments of these components' contributions are further tracked for proactive alerting. An important aspect of this process is the classification of user transactions based on the profile of their resource usage, enabling one to set appropriate thresholds for the different classes only. Combined with the complete or semi-complete dependency information, our approach confines the scope of potential root causes to a small set of components, thus enabling efficient performance problem anticipation and quick remediation.

A New Approach for Recoverable Timestamp Ordering Schedule

Abstract — A new approach for timestamp ordering problem in serializable schedules is presented. Since the number of users using databases is increasing rapidly, the accuracy and needing high throughput are main topics in database area. Strict 2PL does not allow all possible serializable schedules and so does not result high throughput. The main advantages of the approach are the ability to enforce the execution of transaction to be recoverable and the high achievable performance of concurrent execution in central databases. Comparing to Strict 2PL, the general structure of the algorithm is simple, free deadlock, and allows executing all possible serializable schedules which results high throughput. Various examples which include different orders of database operations are discussed. Keywords — Concurrency control, schedule, timestamp, transaction. I. I NTRODUCTION HE number of users who access database applications has been growing so fast and it becomes the highest priority in database fields to handle transactions of users. Researchers have continuously made effort to enhance concurrency control and recovery for centralized database which stored at a single computer site. A centralized DBMS support multiple users, but the DBMS and the database themselves reside totally at a single computer site [10]. Centralized DBMS needs a concurrency control for transaction processing that coordinates the actions of processes that operate in parallel, access shared-data, and therefore potentially interfere with each other. Database products need to be accurate and recoverable. The work load of read and write operation requests the systems to execute a large number of transaction at a given time. In [2], many existing database applications place various timestamps on their data. Timestamp is a unique identifier created by the DBMS to identify a transaction [6]. ts(Ti) refers to transaction with assigned a timestamp i. The timestamp of any transaction is represented by its index (i.e., index(Ti) = i). The index of all operations in Ti is i. Each data item contains a read timestamp, giving the timestamp of the last transactions to read the item and a write timestamp, giving the timestamp of the last transaction to write the item [10]. Timestamp

Web intelligence analyses of digital libraries

Purpose – The purpose of this paper is to explore the use of LexiURL as a Web intelligence tool for collecting and analysing links to digital libraries, focusing specifically on the National electronic Library for Health (NeLH).Design/methodology/approach – The Web intelligence techniques in this study are a combination of link analysis (web structure mining), web server log file analysis (web usage mining), and text analysis (web content mining), utilizing the power of commercial search engines and drawing upon the information science fields of bibliometrics and webometrics. LexiURL is a computer program designed to calculate summary statistics for lists of links or URLs. Its output is a series of standard reports, for example listing and counting all of the different domain names in the data.Findings – Link data, when analysed together with user transaction log files (i.e. Web referring domains) can provide insights into who is using a digital library and when, and who could be using the digital library...

Cluster‐analysis attack against a PRivAte Web solution (PRAW)

PurposeThe purpose of this paper is to prove the ability of PRivAte Web (PRAW) – a system for private web browsing – to stand possible attacks.Design/methodology/approachAttacks on the systems were simulated, manipulating systems variables. A privacy measure was defined to evaluate the capability of the systems to stand the attacks. Analysis of results was performed.FindingsIt was shown that, even if the attack is optimised to provide the attacker's highest utility, the similarity between the user profile and the approximated profile is pretty low and does not enable the eavesdropper to derive an accurate estimation of the user profile.Research limitations/implicationsOne limitation is the “cold start” problem – in the current version, an observer might detect the first transaction, which is always a real user transaction. As a remedy for this problem, the first transaction will be randomly delayed and a random number of fake transactions played before the real one (according to Tr). Another limitation is that PRAW supports only link browsing, originated in search engine interactions (since it is the most common interaction on the web. It should be extended to include concealment of browsing to links originating in the “Favourites” list, that users tend to browse regularly (even a few times a day) for professional or personal reasons.Practical implicationsPRAW is feasible and preserves the privacy of web browsers. It is now undergoing commercialisation to become a shelf tool for privacy preservation.Originality/valueThe paper presents a practical statistical method for privacy preservation and proved that it is standing possible attacks. Methods usually proposed for this problem are not statistical, but cryptography oriented, and are too expensive in processing‐time to be practical.

Optimal Design of Object-Oriented Databases in Automatic Information-Control Systems. II. Models and Methods of Design of Optimal Logical Structures for Object-Oriented Databases

Part II is devoted to the formulation of the problem and models and methods of design of optimal logical structures for object-oriented databases used in designing automatic information control systems. The effectiveness criteria used for the problem design are defined by the minimal total time of utilization of databases and service of a given set of user inquires and transactions, minimal total time of implementation of a set of inquires and transactions over the database. Design problems are formulated as nonlinear integer programming problems and effective exact and heuristic algorithms are developed to solve them.

Design and Analysis of Techniques for Detection of Malicious Activities in Database Systems

Existing host-based Intrusion Detection Systems use the operating system log or the application log to detect misuse or anomaly activities. These methods are not sufficient for detecting intrusion in the database systems. In this paper, we describe a method for detecting malicious activities in a database management system by using data dependency relationships. Typically, before a data item is updated in the database, some other data items are read or written. And after the update, other data items may also be written. These data items read or written in the course of update of a data item construct the read set, prewrite set, and the postwrite set for this data item. The proposed method identifies malicious transactions by comparing these sets with data items read or written in user transactions. We have provided mechanisms for finding data dependency relationships among transactions and use Petri-Nets to model normal data update patterns at user task level. Using this method, we ascertain more hidden anomalies in the database log. Our simulation on synthetic data reveals that the proposed model can achieve desirable performance when both transaction and user task level intrusion detection methods are employed.

Adaptive Update Policy for Proactive Management of Deadline Miss Ratio and Data Freshness in Real-Time Database Models

Important applications, like e-commerce, online stock trading, traffic control demand real-time data services. Conventional database perform poor at these applications. A database for real-time data services has to support timing constraints and temporal consistency in addition to supporting characteristics of a conventional database system. In other words, it is desirable to execute transactions within its deadline using updated data reflecting the current world status. In order to achieve these objectives, the Quality of Service (QoS) metrics like miss ratio, data freshness, perceived freshness and Quality of Data (QoD) management schemes along with architectures for effective QoS management are introduced in Literature. These architectures accommodate the unpredictable change in user transactions and maintain QoS metrics by changing the update pattern of data items, which are periodically updated to reflect the real world status. In this study, we present architecture with an improved update policy and propose an algorithm to change the update pattern in such a manner that the system is able to maintain QoS metrics even under busty traffic conditions. The simulation results show that the proposed model can keep QoS metrics, like miss ratio and perceived freshness within limits under busty traffic conditions [1].

A fuzzy logic-based system for assessing the level of business-to-consumer (B2C) trust in electronic commerce

The purpose of this paper is to present an application of fuzzy logic to human reasoning about electronic commerce (e-commerce) transactions. This paper uncovers some of the hidden relationships between critical factors such as security, familiarity, design, and competitiveness. We analyze the effect of these factors on human decision process and how they affect the Business-to-Consumer (B2C) outcome when they are used collectively. This research provides a toolset for B2C vendors to access and evaluate a user's transaction decision process, and also an assisted reasoning tool for the online user.

Automated analysis of database usage logs in a metasearch environment: The library of Texas experience

AbstractThe poster presents an overview of a research and development effort for a mechanism to produce reliable usage reports for the Library of Texas (LOT) Resource Discovery Service (RDS) metasearch application. The RDS Log Analyzer was designed and implemented by the Texas Center for Digital Knowledge project team as part of a contract with the Texas State Library and Archives Commission to support the LOT RDS. Developers of the RDS application had requirements to implement a mechanism for capturing RDS user transactions in custom log files.

Managing Multiuser Database Buffers Using Data Mining Techniques

In this paper, we propose a data-mining-based approach to public buffer management for a multiuser database system, where database buffers are organized into two areas – public and private. While the private buffer areas contain pages to be updated by particular users, the public buffer area contains pages shared among different users. Unlike traditional buffer management strategies where limited knowledge of user access patterns is used, the proposed approach discovers knowledge from page access sequences of user transactions and uses it to guide public buffer placement and replacement. A prefetch strategy is exploited based on the discovered page access knowledge. In practice, to make such a data-mining-based buffer management approach tractable, we present a soft variation to approximate our absolute best buffer replacement solution. The knowledge to be discovered and the discovery methods are discussed in the paper. The effectiveness of the proposed approach was investigated through a simulation study. The results indicate that with the help of the discovered knowledge, the public buffer hit ratio can be improved significantly, while the added computational complexity, compared to the achievement in buffer hit ratio, is less. In some situations, the time cost of the data-mining-based buffer management policy is even lower than that of the simplest buffer management policy.