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.

A dichotomy in the complexity of deletion propagation with functional dependencies

Abstract: A classical variant of the view-update problem is deletion propagation, where tuples from the database are deleted in order to realize a desired deletion of a tuple from the view. This operation may cause a (sometimes necessary) side effect---deletion of additional tuples from the view, besides the intentionally deleted one. The goal is to propagate deletion so as to maximize the number of tuples that remain in the view. In this paper, a view is defined by a self-join-free conjunctive query (sjf-CQ) over a schema with functional dependencies. A condition is formulated on the schema and view definition at hand, and the following dichotomy in complexity is established. If the condition is met, then deletion propagation is solvable in polynomial time by an extremely simple algorithm (very similar to the one observed by Buneman et al.). If the condition is violated, then the problem is NP-hard, and it is even hard to realize an approximation ratio that is better than some constant; moreover, deciding whether there is a side-effect-free solution is NP-complete. This result generalizes a recent result by Kimelfeld et al., who ignore functional dependencies. For the class of sjf-CQs, it also generalizes a result by Cong et al., stating that deletion propagation is in polynomial time if keys are preserved by the view.

Protecting confidential information in a database for enabling targeted advertising in a communications network

Abstract: Protecting a database against the deduction of confidential values contained therein is accomplished by partitioning the database into public and private values (202), some of which public values are deemed more important than others (203). The private attribute values are electronically processed (204-226) to reduce any high correlation between the public values and the private values. Specifically the processor partitions the database (204-210) into safe tuples and unsafe tuples, which unsafe tuples have high correlative public values (216-218). The processor then selectively combines the public attribute values of the tuples (220) to camouflage such tuples from deduction of their private attribute values beyond a threshold level of uncertainty (226).

Fitting Pattern Structures to Knowledge Discovery in Big Data

Abstract: Pattern structures, an extension of FCA to data with com- plex descriptions, propose an alternative to conceptual scaling (binariza- tion) by giving direct way to knowledge discovery in complex data such as logical formulas, graphs, strings, tuples of numerical intervals, etc. Whereas the approach to classification with pattern structures based on preceding generation of classifiers can lead to double exponent complex- ity, the combination of lazy evaluation with projection approximations of initial data, randomization and parallelization, results in reduction of algorithmic complexity to low degree polynomial, and thus is feasible for big data.

Reconciling skyline and ranking queries

Abstract: Traditionally, skyline and ranking queries have been treated separately as alternative ways of discovering interesting data in potentially large datasets. While ranking queries adopt a specific scoring function to rank tuples, skyline queries return the set of non-dominated tuples and are independent of attribute scales and scoring functions. Ranking queries are thus less general, but usually cheaper to compute and widely used in data management systems.We propose a framework to seamlessly integrate these two approaches by introducing the notion of restricted skyline queries (R-skylines). We propose R-skyline operators that generalize both skyline and ranking queries by applying the notion of dominance to a set of scoring functions of interest. Such sets can be characterized, e.g., by imposing constraints on the function's parameters, such as the weights in a linear scoring function. We discuss the formal properties of these new operators, show how to implement them efficiently, and evaluate them on both synthetic and real datasets.

O(log n) dynamic router-tables for prefixes and ranges

Abstract: Two versions of the Internet (IP) router-table problem are considered. In the first, the router table consists of n pairs of tuples of the form (p, a), where p is an address prefix and a is the next-hop information. In this version of the router-table problem, we are to perform the following operations: insert a new tuple, delete an existing tuple, and find the tuple with longest matching-prefix for a given destination address. We show that each of these three operations may be performed in O(log n) time in the worst case using a priority-search tree. In the second version of the router-table problem considered by us, each tuple in the table has the form (r, a), where r is a range of destination addresses matched by the tuple. The set of tuples in the table is conflict-free. For this version of the router-table problem, we develop a data structure that employs priority-search trees as well as red-black trees. This data structure permits us to perform each of the operations insert, delete, and find the tuple with most-specific matching-range for a given destination address in O(log n) time each in the worst case. The insert and delete operations preserve the conflict-free property of the set of tuples. Experimental results are also presented.