Cross Language Duplicate Record Detection in Big Data

Duplicate record detection is a crucial task for data cleaning process in data warehouse systems. Many approaches have been presented to address this problem: some of these rely on the accuracy of the resulted records, others focus on the efficiency of the comparison process. Following the first direction, we introduce two similarity functions based on the concept of q-grams that contribute to improve accuracy of duplicate detection process with respect to other well known measures. We also reduce the number and the running time of record comparisons by building an inverted index on a sorted list of q-grams, named q-grams array. Then, we extend this approach to perform a clustering process based on the proposed q-grams array. Finally, an experimental analysis on synthetic and real data shows the efficiency of the novel indexing method for both record comparison process and clustering.

Reasoning about attribute value equivalence in relational data

Duplicate detection identifies multiple records in a dataset that represent the same real-world object. Many such approaches exist, both in research and in industry. To investigate essential properties of duplicate detection algorithms, such as their result quality or runtime behavior, they must be executed on suitable test data. The quality evaluation requires that these test data are labeled, constituting a ground truth. Correctly labeled, sizable, and real or at least realistic test datasets, however, are not easy to obtain, creating an obstacle for the advancement of research. In this tutorial, we present common methods to evaluate duplicate detection algorithms and to generate labeled test data. We close with a discussion of open problems.

Foundations of SQL Server 2008 R2 Business Intelligence introduces the exciting gamut of business intelligence tools included with SQL Server 2008. Microsoft has designed SQL Server 2008 to be more than just a database. Its a complete business intelligence (BI) platform. The database is at the core, and surrounding the database are tools for data mining, modeling, reporting, analyzing, charting, and integration with other, enterprise-level software packages. SQL Server 2008 puts an incredible amount of BI functionality at your disposal. But how do you take advantage of it? Thats what this book is all about. Authors Guy Fouch and Lynn Langit show how to implement end-to-end BI solutions using SQL Server Analysis Services (SSAS), SQL Server Integration Services (SSIS), SQL Server Reporting Services (SSRS), and other tools in the Microsoft BI toolkit. Youll learn about all-new features such as PowerPivot and Report Builder 3.0. Also provided are clear examples of predictive analysis made possible through powerful data mining features in SQL Server. If youre an analyst or developer working with SQL Server 2008 who is charged with delivering results that drive business success, you cant afford to be without this book; you cant afford to ignore the powerful BI suite that Microsoft has placed at your disposal. Provides the big picture of Microsofts BI tool suite Covers PowerPivot and other game-changing technologies introduced alongside SQL Server 2008 Release 2 Gives a practical analysis of features based on real-world practices What youll learn Apply dimensional- and fact-based modeling to create OLAP schemas Optimize data extraction and transformation with SQL Server Integration Services Develop OLAP and data-mining solutions using SQL Server Analysis Services Create compelling reports using the new Report Builder 3.0 in SQL Server Reporting Services Deliver self-service business-intelligence via Microsoft PowerPivot Manage and maintain control over your BI environment Who this book is for Foundations of SQL Server 2008 R2 Business Intelligence is for business intelligence analysts and developers wanting to employ the full stack of business intelligence tools that are part of SQL Server. The book also appeals to database administrators managing databases in support of business intelligence efforts. Table of Contents What Is Business Intelligence? OLAP Modeling Concepts Introducing OLAP Modeling with SSAS Intermediate OLAP Modeling with SSAS Advanced OLAP Modeling with SSAS Cube Storage and Aggregation Introducing SSIS Intermediate SSIS Advanced SSIS Reporting Tools Data Mining with Excel Introducing PowerPivot Introduction to MDX Introduction to Data Mining

In this paper, we will propose PC-Filter (PC stands for Partitio n Comparison), a robust data filter for approximately duplicate record detection in large databases. PC-Filter distinguishes itself from all of existing methods by using the notion of partition in duplicate detection. It first sorts the whole database and splits the sorted database into a number of record partitions. The Partition Comparison Graph (PCG) is then constructed by performing fast partition pruning. Finally, duplicate records are effectively detected by using internal and external partition comparison based on PCG. Four properties, used as heuristics, have been devised to achieve a remarkable efficiency of the filter based on triangle inequity of record similarity. PC-Filter is insensitive to the key used to sort the database, and can achieve a very good recall level that is comparable to that of the pair-wise record comparison method but only with a complexity of O(N 4/3). Equipping existing detection methods with PC-Filter, we are able to well solve the ”Key Selection” problem, the ”Scope Specification” problem and the ”Low Recall” problem that the existing methods suffer from.

Data quality is a key factor for economical success. It is usually defined as a set of properties of data, such as completeness, accessibility, relevance, and conciseness. The latter includes the absence of multiple representations for same real world objects. To avoid such duplicates, there is a wide range of commercial products and customized self-coded software. These programs can be quite expensive both in acquisition and maintenance. In particular, small and medium-sized companies cannot afford these tools. Moreover, it is difficult to set up and tune all necessary parameters in these programs. Recently, web-based applications for duplicate detection have emerged. However, they are not easy to integrate into the local IT landscape and require much manual configuration effort.With DAQS (Data Quality as a Service) we present a novel approach to support duplicate detection. The approach features (1) minimal required user interaction and (2) self-configuration for the provided input data. To this end, each data cleansing task is classified to find out which metadata is available. Next, similarity measures are automatically assigned to the provided records’ attributes and a duplicate detection process is carried out. In this paper we introduce a novel matching approach, called one-to-some or 1:k assignment, to assign similarity measures to attributes. We performed an extensive evaluation on a large training corpus and ten test datasets of address data and achieved promising results.KeywordsDatabase ManagementDatabase ApplicationsData miningMatchingIntrinsicData Qualitymatchingduplicate detectionsimilarity measuresdata cleansing

Purpose – The purpose of this paper is to focus on duplicate record detection algorithms used for detection in bibliographic databases.

Humans automatically attend to certain objects in a scene. Better understanding this process could improve a computer's ability to parse scene images and convey information about them to humans. This thesis is arranged in three parts. The first part explores how important a particular object is in a photograph of a complex scene. We propose a definition of importance and present two methods for measuring object importance from human observers. Using this ground truth, we fit a function for predicting the importance of each object directly from a segmented image; our function combines many object-related and image-related features. We validate our importance predictions on a large set of objects and find that the most important objects may be identified automatically. We find that object position and size are particularly informative, while a popular measure of saliency is not. The second part explores the relationship between object naming, eye movements, and saliency maps. Eye movements correlate with shifts in attention and are thought to be a consequence of optimal resource allocation for high-level tasks such as visual recognition. Saliency maps, are often built on the assumption that features (e.g., color, contrast, orientation, and motion) as opposed to objects themselves drive attention. We measure the eye position of humans viewing scenes and then ask them to recall objects that they saw in each scene. Weighted with recall frequency or maximum saliency, these objects predict fixations in individual images better than early saliency, suggesting that early saliency may have an indirect effect on attention, acting through detected objects. The third part explores the problem of locating objects in a scene irrespective of category. We introduce the first benchmark for category-independent object detection. It is composed of a large public dataset of annotated high-resolution scene images and suitable metrics for performance evaluation. We demonstrate our benchmark by comparing three methods for generalized object detection against a baseline and an upper bound.

SummaryData Deduplication is the foremost technique used for data compression by removing redundant data. It is broadly utilized for cloud storage server, which reduces the un‐utilized memory space and optimized bandwidth. The confidentiality of sensitive information maintains during Deduplication detection. The Efficient Hash Function–based Duplication Detection (EHFDD) algorithm is proposed to encrypt the content before outsourcing in cloud environment. This paper is an attempt to enhance the data security and notify the issues of approved data Deduplication detections. Furthermore, it also presents several novel Deduplication improvisations for helping certified duplicate verification in hybrid cloud. The EHFDD method enables duplicate detection scheme with minimal overhead compared to existing functions. Based on experimental evaluations, proposed EHFDD algorithm reduces the average delay in 28.7 milliseconds, memory utilization in 8%, computation time in 457 milliseconds, communications overhead in 900 milliseconds, and improves the success rate in 3.13%.

The increasing popularity of internet of things (IoT), dissimilar networks, distributed devices, and applications has turned out to be a major call for the identification of novel security threats and tracing malicious network behaviours. An intrusion detection system (IDS) is a self-defense tool for preventing several types of cyberattacks. Latest machine learning (ML) methods are becoming the backbone for constructing intelligent IDS that are highly data driven. This chapter proposes decision tree based IDS for the dataset NSL-KDD. A novel approach has been developed for the ranking of security features. The proposed system has been validated against performance evaluation metrics consisting of recall, precision, accuracy, and F score. The results produced by the proposed system are compared with well-known ML methods including logistic regression, support vector machines and K-nearest neighbor in order to analyse the efficiency.

The paper presents ORIENT-Net as a tool that processes texts in terms of their orientalist views. This is done by the detection and classification of such texts on social media Twitter. An Orientalist view of the East can see the cultures there as being quaint, obsolete, or entirely unlike the West, which Westerners can be very often influenced by. The demographic figures are also computed for the ORIENT-Net project, to be able to provide the necessary NLP, ML, and data mining solutions that are tailored to the specific demographics. The framework collects the relevant tweets on the basis of keywords that also cover a range of different kinds of Orientalist statements. The very subtle orientalist markers are identified through three linguistic features: TF-IDF, n-grams, and BERT embeddings that are the result of fine-tuning. The CatBoost classifier is the one that does the classification of the tweets into ‘orientalist’ and ‘non-orientalist’ categories, and the system’s performance is measured through accuracy evaluation, along with precision and F1-score assessments. The model relies on SHAP and LIME for interpretability and decision explanation. The framework makes online detection of oriental discourse automatic. The results indicate the high accuracy and clarity of orientalist identification. This system offers a scalable tool to support sociocultural research and inform evidence-based policy decisions. The uniqueness of this research is that it combines postcolonial theory and computational modelling via the ORIENT-Net framework, thus providing an interpretable and scalable method for online ideological discourse analysis. Future work will focus on expanding the dataset across multiple languages and platforms, as well as refining detection capabilities to capture more nuanced and context-dependent discourse patterns.

Student academic records management and assessment are among the vital processes in any higher education institution. These processes must be efficient and systematic. The significant problems of the current system that is holding the student’s academic record have been observed and identified. Based upon the limitations and the challenges on the existing procedures of a manual storing and archiving of the students’ academic records, a centralized students’ academic record management system was proposed. This project primarily aimed to develop a computerized student academic record management system in order to help DMC College Foundation Incorporated (DMCCFI) Philippines improve its performance in the registrar services, specifically in the assessment and evaluation of student records. The newly developed system is integrated with business intelligence. It offers modules such as grade submission, assessment, and student kiosk with security feature. The development model included Rapid Application Development (RAD). The tools used in the development are Windows Server 2008 R2, DevExpress, Microsoft Visual Studio C#, MSSQL, and SmartDraw. Based on the system evaluation, the proposed system is very good with an aggregate mean of 4.55. The result implies that the MIS staff, coordinators and dean strongly agree that the proposed system is highly acceptable. In terms of usability, the proposed system is rated with an aggregate mean of 6.43 described as “Strongly Agree.” It was concluded that the developed system offers the capabilities and functions that can help improve the performance of the registrar services in DMCCFI and is highly recommended for immediate utilization by DMCCFI.

Hybrid-biomarker case-based reasoning system for water pollution assessment in Abou Hammad Sharkia, Egypt

This paper initiates a systematic methodology for real-time chaos-based video encryption and decryption communications on the system design and algorithm analysis. The proposed system design and algorithm analysis have been validated on an FPGA hardware platform via Verilog Hardware Description Language (Verilog HDL). Based on the fundamental anti-control principles of dynamical systems, a 6-D real domain chaotic system is designed, and then the corresponding Verilog HDL algorithm is developed. The proposed Verilog HDL algorithm is utilized to design a real-time chaos-based secure video communication system, with a generalized design principle derived, which is implemented on an FPGA hardware platform equipped with an XUP Virtex-II chip. Following this line, the designed working mechanism is demonstrated by hardware experiments. The security performance is tested using the TESTU01 statistical test suites, the differential analysis, and the sensitivity of key parameters mismatch. Both theoretical analysis and experimental results validate the feasibility and reliability of the proposed system.

The paper presents a novel method of design and implementation of a control system using Bluetooth technologies. The proposed system based on IOIO board, Bluetooth and android application endeavours to support user the ability to control the electrical devices from mobile devices, which must have android operating system. The proposed system design is simple, multifunctional, superior to previous approaches and can considerably economise the costs involved in developing such systems. The application has a variety of uses in offices, factories, laboratories and access controlled environments.KeywordsAndroidIOIO boardBluetoothSDK toolsATD plugin