Tuples Of Values Research Articles

BUNNI: Learning Repair Actions in Rule-driven Data Cleaning

In this work, we address the challenging and open problem of involving non-expert users in the data repairing problem as first-class citizens. Despite a large number of proposals that have been devoted to cleaning data from the point of view of expert users (IT staff and data scientists), there is a lack of studies from the perspective of non-expert ones. Given a set of available data quality rules, we exploit machine learning techniques to guide the user to identify the dirty values for each violation and repair them. We show that with a low user effort, it is possible to identify the values in tuples that can be trusted and the ones that are most likely errors. We show experimentally how this machine learning approach leads to a unique clean solution with high quality in scenarios where other approaches fail.

Discovery of Cross Joins

A cross join between two attribute sets holds on a relation whenever its projection onto the union of the attribute sets is the cross join between its projections on the first and second attribute set. Hence, the cross join is a fundamental operator on database relations. For example, it can rewrite the division operator into a simple projection, or measure the independence of tuple values between two attribute sets during cardinality estimation. It is therefore surprising that we present the first research on the discovery problem of cross joins. We show that the problem of deciding whether there is a cross join that holds on a given relation is not only NP-complete but W[3]-complete in its arguably most natural parameter, namely its arity. We establish the first algorithms that discover all cross joins that hold on a given relation. We illustrate in experiments with benchmark data that our algorithms perform well within the limits established by our hardness results. Our treatment of cross joins and the design of our algorithms enables us to extend our findings to the discovery of cross joins that meet a given approximation ratio. Our experiments quantify the trade-off between discovery time and targeted ratio.

Minimal contact circuits for characteristic functions of spheres

Abstract We study the complexity of implementation of the characteristic functions of spheres by contact circuits. By the characteristic functions of the sphere with center at a vertex σ̃ = (σ 1, …, σn ), σ 1, …, σn ∈ {0, 1}, we mean the Boolean function $\begin{array}{} \varphi^{(n)}_{\tilde\sigma} \end{array} $ (x 1, …, xn ) which is equal to 1 on those and only those tuples of values that differ from the tuple σ̃ only in one digit. It is shown that the number 3n − 2 of contacts is necessary and sufficient for implementation of $\begin{array}{} \varphi^{(n)}_{\tilde\sigma} \end{array} $ (x̃) by a contact circuit.

Digital Watermarking for Anonymized Data With Low Information Loss

At present, massive amounts of data are utilized for artificial intelligence technologies such as machine learning and deep learning. However, these data must be utilized carefully while preserving data privacy. Data anonymization is a technique enabling both data mining and privacy protection, preventing the identification of individuals by generalizing the data to include multiple records with the same values. In this study, we consider a data-publishing infrastructure for personal data sharing. The infrastructure anonymizes data prior to publishing it to users for privacy protection; however, the problem of unauthorized republishing by malicious users must be considered. To address this issue, we studied digital watermarking methods that correlate data users with anonymized data. Our previous method embedded information indicating the original user to detect illegally republished data. However, this method did not focus on information loss. This study proposes another digital watermarking method for anonymized data that achieves low information loss. The proposed method replaces values in tuples to embed information. To reduce the information loss caused by the embedding, the proposed method selects replacement values from the candidates whose meanings are similar to the original. We propose the use of vector-conversion tables to select replacement values. The proposed method also extends the maximum length of the embedded bit string by embedding multiple bits into a single tuple. Moreover, we measured the tolerance to distortion attacks to evaluate the efficacy of the proposed method. The proposed method is non-blind, i.e., data prior to digital watermarking is required to perform extraction.

General definitions for the union and intersection of ordered fuzzy multisets

Since its original formulation, the theory of fuzzy sets has spawned a number of extensions where the role of membership values in the real unit interval $[0, 1]$ is handed over to more complex mathematical entities. Amongst the many existing extensions, two similar ones, the fuzzy multisets and the hesitant fuzzy sets, rely on collections of several distinct values to represent fuzzy membership, the key difference being that the fuzzy multisets allow for repeated membership values whereas the hesitant fuzzy sets do not. But in neither case are these collections of values ordered, as they are simply represented through multisets or sets. In this paper, we study ordered fuzzy multisets, where the membership value can be an ordered $n$-tuple of values, thus accounting for both order and repetition. We present some basic definitions and results and explore the relation between these ordered fuzzy multisets and the fuzzy multisets and hesitant fuzzy sets.

A New Aggregated Attribute Values Match Technique for Improving the Quality of Probability Estimated Decision Trees

Probability estimations of decision trees may not be useful directly because their poor probability estimations but the best probability estimations are desired in many useful applications. Many techniques have been proposed for obtaining good probability estimations of decision trees. Two such optical techniques are identified and the first one is single tree based aggregation of mismatched attribute values of instances. The second one is bagging technique but it is costly and less comprehensible. So, in this paper a single aggregated probability estimation decision tree model technique is proposed for improving the performance of probability estimations of decision trees and the performance of new technique is evaluated using area under the curve (AUC) evaluation technique. The proposed technique computes aggregate scores based on matched attribute values of test tuples.

On the Complexity of Multivalued Logic Functions over Some Infinite Basis

Under study is the complexity of the realization of k-valued logic functions (k ≥ 3) by logic circuits in the infinite basis consisting of the Post negation (i.e., the function (x + 1) mod k) and all monotone functions. The complexity of the circuit is the total number of elements of this circuit. For an arbitrary function f, we find the lower and upper bounds of complexity, which differ from one another at most by 1 and have the form 3 log3(d(f)+ 1)+O(1), where d(f) is the maximal number of the decrease of the value of f taken over all increasing chains of tuples of values of the variables. We find the exact value of the corresponding Shannon function which characterizes the complexity of the most complex function of a given number of variables.

Heuristic-based hybrid privacy-preserving data stream mining approach using SD-Perturbation and Multi-Iterative K-Anonymization

Different e-sources are regularly generating huge volumes of data. Data mining is the technique of gathering knowledge from a dataset, but dataset often contains sensitive information so discharging such data may cause privacy breaches. The problem of privateness desires to be is addressed earlier than streaming facts are launched for mining and evaluation functions. Various algorithms proposed so far have focused mainly on static data and very few are on data streams. Perturbation and k-anonymity have received significant attention over other privacy-preserving techniques because of its easiness and effectiveness in guarding data. The proposed hybrid approach is an extension to heuristic-based data perturbation where privacy is preserved through computed tuple values for each instance and users define sensitive drift (SD) and an extension to k-anonymisation where privacy gain has been worked out for choosy anonymisation for a set of tuples and perturbs the sensitive attribute values on data streams.

Classification algorithm based on pairwise comparison of features

We propose an alternative approach to classification that differs from known approaches in that instead of comparing the tuple of values of a test object’s features with similar tuples of features for objects in the training set, in this approach we make independent pairwise comparisons of every pair of feature values for the objects being compared. Here instead of using the notion of a “nearest neighbors” for test object, we introduce the notion of “admissible proximity” for each feature value in the test object. In this approach, we propose an alternative algorithm for classification that has a number of significant practical features. The algorithm’s quality was evaluated on sample problems taken from the well-known UCI repository and related to various aspects of human activity. The results show that the algorithm is competitive compared to known classification algorithms.

BayesWipe

Recent efforts in data cleaning of structured data have focused exclusively on problems like data deduplication, record matching, and data standardization; none of the approaches addressing these problems focus on fixing incorrect attribute values in tuples. Correcting values in tuples is typically performed by a minimum cost repair of tuples that violate static constraints like Conditional Functional Dependencies (which have to be provided by domain experts or learned from a clean sample of the database). In this article, we provide a method for correcting individual attribute values in a structured database using a Bayesian generative model and a statistical error model learned from the noisy database directly. We thus avoid the necessity for a domain expert or clean master data. We also show how to efficiently perform consistent query answering using this model over a dirty database, in case write permissions to the database are unavailable. We evaluate our methods over both synthetic and real data.

Higher-order and tuple-based massively-parallel prefix sums

Prefix sums are an important parallel primitive, especially in massively-parallel programs. This paper discusses two orthogonal generalizations thereof, which we call higher-order and tuple-based prefix sums. Moreover, it describes and evaluates SAM, a GPU-friendly algorithm for computing prefix sums and other scans that directly supports higher orders and tuple values. Its templated CUDA implementation unifies all of these computations in a single 100-statement kernel. SAM is communication-efficient in the sense that it minimizes main-memory accesses. When computing prefix sums of a million or more values, it outperforms Thrust and CUDPP on both a Titan X and a K40 GPU. On the Titan X, SAM reaches memory-copy speeds for large input sizes, which cannot be surpassed. SAM outperforms CUB, the currently fastest conventional prefix sum implementation, by up to a factor of 2.9 on eighth-order prefix sums and by up to a factor of 2.6 on eight-tuple prefix sums.

Semi-Supervised Bootstrapping Approach for Named Entity Recognition

The aim of Named Entity Recognition (NER) is to identify references of named entities in unstructured documents, and to classify them into pre-defined semantic categories. NER often aids from added background knowledge in the form of gazetteers. However using such a collection does not deal with name variants and cannot resolve ambiguities associated in identifying the entities in context and associating them with predefined categories. We present a semi-supervised NER approach that starts with identifying named entities with a small set of training data. Using the identified named entities, the word and the context features are used to define the pattern. This pattern of each named entity category is used as a seed pattern to identify the named entities in the test set. Pattern scoring and tuple value score enables the generation of the new patterns to identify the named entity categories. We have evaluated the proposed system for English language with the dataset of tagged (IEER) and untagged (CoNLL 2003) named entity corpus and for Tamil language with the documents from the FIRE corpus and yield an average f-measure of 75% for both the languages.

Ontology-guided distortion control for robust-lossless database watermarking: application to inpatient hospital stay records.

In this paper, we propose a new semantic distortion control method for database watermarking. It is based on the identification of the semantic links that exist in-between attribute's values in tuples by means of an ontology. Such a database distortion control provides the capability for any watermarking scheme to avoid incoherent records and consequently ensures: i) the normal interpretation of watermarked data, i.e. introducing a watermark semantically imperceptible; ii) prevent the identification by an attacker of watermarked tuples. The solution we present herein successfully combines this semantic distortion control method with a robust lossless watermarking scheme. Experimental results conducted on a medical database of more than one half million of inpatient hospital stay records also show a non-negligible gain of performance in terms of robustness and database distortion.

Classification of Uncertain Data Streams Based on Extreme Learning Machine

Classification over data streams is an important task in data mining. The challenges become even larger when uncertain data are considered. An important challenge in the classification of uncertain data streams is concept drift and uncertainty of data. This paper studies the problem using extreme learning machine (ELM). We first propose weighted ensemble classifier based on ELM (WEC-ELM) algorithm, which can dynamically adjust classifier and the weight of training uncertain data to solve the problem of concept drift. Furthermore, an uncertainty classifier based on ELM (UC-ELM) algorithm is designed for the classification of uncertain data streams, which not only considers tuple value, but also its uncertainty, improving the efficiency and accuracy. Finally, the performance of our methods is verified through a large number of simulation experiments. The experimental results show that our methods are effective ways to solve the problem of classification of uncertain data streams and are able to solve the problem of concept drift, reduce the execution time and improve the efficiency.

Grid-PPPS: A Skyline Method for Efficiently Handling Top-kQueries in Internet of Things

A rapid development in wireless communication and radio frequency technology has enabled the Internet of Things (IoT) to enter every aspect of our life. However, as more and more sensors get connected to the Internet, they generate huge amounts of data. Thus, widespread deployment of IoT requires development of solutions for analyzing the potentially huge amounts of data they generate. A top-kquery processing can be applied to facilitate this task. The top-kqueries retrievektuples with the lowest or the highest scores among all of the tuples in the database. There are many methods to answer top-kqueries, where skyline methods are efficient when considering all attribute values of tuples. The representative skyline methods are soft-filter-skyline (SFS) algorithm, angle-based space partitioning (ABSP), and plane-project-parallel-skyline (PPPS). Among them, PPPS improves ABSP by partitioning data space into a number of spaces using hyperplane projection. However, PPPS has a high index building time in high-dimensional databases. In this paper, we propose a new skyline method (called Grid-PPPS) for efficiently handling top-kqueries in IoT applications. The proposed method first performs grid-based partitioning on data space and then partitions it once again using hyperplane projection. Experimental results show that our method improves the index building time compared to the existing state-of-the-art methods.

Tuple Value Based Multiplicative Data Perturbation Approach to Preserve Privacy in Data Stream Mining

Huge volume of data from domain specific applications such as medical, financial, library, telephone, shopping records and individual are regularly generated. Sharing of these data is proved to be beneficial for data mining application. On one hand such data is an important asset to business decision making by analyzing it. On the other hand data privacy concerns may prevent data owners from sharing information for data analysis. In order to share data while preserving privacy, data owner must come up with a solution which achieves the dual goal of privacy preservation as well as an accuracy of data mining task - clustering and classification. An efficient and effective approach has been proposed that aims to protect privacy of sensitive information and obtaining data clustering with minimum information loss.

The Connection between Concepts in Formal Concept Analysis and Concepts in a Dual Description Logic

Formal concept analysis (FCA) and description logics (DLs) are two major formalisms around concepts. However, concepts in FCA and concepts in DLs are of different nature. The former are tuples/properties pairs in a given relation, the later are just subsets of domains in terminological interpretations. To represent the logical difference of tuples and attribute values in relations, a dual description logic (DDL) is introduced, in which constants are classified into two classes: tuple constants and value constants, and they are interpreted into two disjoint parts in domain, respectively. Given a model M for DDL, we can obtain a normal relation RM. This paper demonstrates how to connect FCA-concepts in RM and concepts in DDL. To do so, we introduce logical operations negation, disjunction and conjunction on values and on attribute-value pairs, and then build Φ-statements from attribute-value pairs inductively with these operations, and further define a kind of extended FCA-concepts, called Γ-concepts. Each Γ-concept consists of two parts: the extent (tuples the concept covers) and the intent (Φ-statements describing the concept), and there are the following results: the union of any number of extents is always an extent; the complement of an extent is always an extent. Consequently, we obtain two isomorphic lattices: one is LM=(Θ, ∪,∩,–), which is the lattice of subsets of Θ, and the other is concept lattice L(↑RM), where Θ consists of the interpretations of DDL-concepts, and ↑RM is an extend relation from the normal relation RM.

Efficient and scalable continuous skyline monitoring in two-tier streaming settings

Two-tier streaming settings are a typical dynamic environment where continuous skylines represent an important semantic indicator for multiple attributes. To monitor skylines over the dynamic data in such settings, one needs to continuously update the skyline query results in order to reflect the new data values. This paper tackles the problem of continuous skyline monitoring on a central query server over dynamic data from multiple data sites. Simply sending the updates of tuple values to the server is cost-prohibitive. Therefore, we propose an approach that allows the central server to collaborate with the data sites to monitor the possible skyline changes. By doing so, the processing load is distributed over all the data sites instead of only on the central server. Furthermore, this collaborative approach minimizes the bandwidth consumption between the server and the data sites, which is often critical in a widely distributed environment such as a wide-area sensor network. We give theoretical upper bounds for the computation costs and communication costs of the proposed collaborative approach. We also conduct extensive experiments on both synthetic and real data sets. The experimental results demonstrate that our collaborative approach is efficient, scalable and well-balanced in terms of communication costs and computation costs.

T‐wise combinatorial interaction test suites construction based on coverage inheritance

SUMMARYCombinatorial interaction testing (CIT) is a testing technique that requires covering all t‐sized tuples of values out of n parameter attributes or properties modelled after the input parameters or the configuration domain of a system under test. CIT test suites have shown to be very effective in software testing already at pairwise (t = 2) level, and the effectiveness of CIT grows with the tuple width t. Unfortunately, the number of tuples to be tested also does grow. In order to reduce the testing effort, researchers addressed the issue of computing minimal‐sized CIT test suites with effective and scalable algorithms. However, still very few generally applicable t‐wise covering construction algorithms (and tools) do exist in literature. This paper presents an original greedy algorithm to compute t‐wise covering mixed covering arrays with constant space complexity, irrespective of the number of involved parameters and strength of interaction. The proposed algorithm has been implemented in a prototype tool, featuring also support for user constraints over the inputs. Assessment of the tool performance on a set of large, real‐world test systems is reported, with results encouraging its adoption in industrial production environments. Copyright © 2011 John Wiley & Sons, Ltd.

Privacy Preserving Frequency Mining in 2-Part Fully Distributed Setting

Recently, privacy preservation has become one of the key issues in data mining. In many data mining applications, computing frequencies of values or tuples of values in a data set is a fundamental operation repeatedly used. Within the context of privacy preserving data mining, several privacy preserving frequency mining solutions have been proposed. These solutions are crucial steps in many privacy preserving data mining tasks. Each solution was provided for a particular distributed data scenario. In this paper, we consider privacy preserving frequency mining in a so-called 2-part fully distributed setting. In this scenario, the dataset is distributed across a large number of users in which each record is owned by two different users, one user only knows the values for a subset of attributes, while the other knows the values for the remaining attributes. A miner aims to compute the frequencies of values or tuples of values while preserving each user's privacy. Some solutions based on randomization techniques can address this problem, but suffer from the tradeoff between privacy and accuracy. We develop a cryptographic protocol for privacy preserving frequency mining, which ensures each user's privacy without loss of accuracy. The experimental results show that our protocol is efficient as well.