Tuple

About: Tuple is a research topic. Over the lifetime, 6513 publications have been published within this topic receiving 146057 citations. The topic is also known as: tuple & ordered tuplet.

DFD: Efficient Functional Dependency Discovery

Abstract: The discovery of unknown functional dependencies in a dataset is of great importance for database redesign, anomaly detection and data cleansing applications. However, as the nature of the problem is exponential in the number of attributes none of the existing approaches can be applied on large datasets. We present a new algorithm DFD for discovering all functional dependencies in a dataset following a depth-first traversal strategy of the attribute lattice that combines aggressive pruning and efficient result verification. Our approach is able to scale far beyond existing algorithms for up to 7.5 million tuples, and is up to three orders of magnitude faster than existing approaches on smaller datasets.

Mining knowledge rules from databases: a rough set approach

Abstract: The principle and experimental results of an attribute oriented rough set approach for knowledge discovery in databases are described. Our method integrates the database operation, rough set theory and machine learning techniques. In this method the learning procedure consists of two phases: data generalization and data reduction. In the data generalization phase, attribute oriented induction is performed attribute by attribute using attribute removal and concept ascension, some undesirable attributes to the discovery task are removed and the primitive data is generalized to the desirable level; thus a set of tuples may be generalized to the same generalized tuple. This procedure substantially reduces the computational complexity of the database learning process. Subsequently, in data reduction phase, the rough set method is applied to the generalized relation to find a minimal attribute set relevant to the learning task. The generalized relation is reduced further by removing those attributes which are irrelevant and/or unimportant to the learning task. Finally the tuples in the reduced relation are transformed into different knowledge rules based on different knowledge discovery algorithms. Based upon these principles, a prototype knowledge discovery system, DBROUGH has been constructed. In DBROUGH, a variety of knowledge discovery algorithms are incorporated and different kinds of knowledge rules, such as characteristic rules, classification rules, decision rules, maximal generalized rules can be discovered efficiently and effectively from large databases.

Method for creating a scalable graph database

Abstract: Embodiments of a method for creating a graph database which is arranged to store or process data in the form of graph tuples comprising n-parts, are described. In an embodiment, each tuple-part is encoded into a unique part identifier (hereinafter called a UPI), each UPI comprises a tag at a fixed position within the UPI. The tag indicates the datatype of the encoded tuple-part. The content data for the tuple-part is encoded in a code that is configured to reflect the ranking or order of the content data, corresponding to each datatype, relative to other tuples in a set of tuples. For content data that comprises a character-string, the code comprises a hashcode; and for content data that comprises or includes a numeric value, the code comprises an immediate value that directly stores the numeric value without encoding.

Privacy-Preserving Inter-Database Operations

Abstract: We present protocols for distributed computation of relational intersections and equi-joins such that each site gains no information about the tuples at the other site that do not intersect or join with its own tuples. Such protocols form the building blocks of distributed information systems that manage sensitive information, such as patient records and financial transactions, that must be shared in only a limited manner. We discuss applications of our protocols, outlining the ramifications of assumptions such as semi-honesty. In addition to improving on the efficiency of earlier protocols, our protocols are asymmetric, making them especially applicable to applications in which a low-powered client interacts with a server in a privacy-preserving manner. We present a brief experimental study of our protocols.