Processing costs in distributed environments is most often dominated by the network communications required for interprocess communication. It is well-known from distributed relational database design research that careful placement of data "near" the users or processors where it is used is mandatory or system performance will suffer greatly. Data placement in relational database systems is comparatively simple because the data is flat, structured, and passive. Objects are characterized by an inheritance hierarchy (other hierarchies could also be considered including, class composition and execution), unstructured (possibly dynamic data), and contain a behavioral component that defines how the "data" is accessed by encapsulating it within the object per se. Algorithms currently exist for fragmenting relations, but the fragmentation and allocation of objects is still a relatively untouched field of study. Similar to relations, objects can be fragmented both horizontally and ve...

Recently much work has been done towards extending object-oriented database systems (OODBs) with advanced tools such as view technology, advanced schema evolution support, and role modeling systems. These extensions all require that the underlying database system supports more powerful and flexible modeling constructs than are currently supported by existing OODB systems. In this paper, we identify these features as multiple classification, dynamic reclassification, and dynamic restructuring. We then describe a methodology known as object-slicing that is capable of extending data models to support these required features. We have successfully implemented an object-slicing software layer using the GemStone system, which while still providing full access to all GemStone DBMS functions, now also offers all required modeling features. In this paper, we describe our experimental results evaluating the relative costs and benefits of adopting the object-slicing technique. This includes an an...

Abstract. Privacy requirements have an increasing impact on the realization of modern applications. Technical considerations and many significant commercial and legal regulations demand today that privacy guarantees be provided whenever sensitive information is stored, processed, or communicated to external parties. It is therefore crucial to design solutions able to respond to this demand with a clear integration strategy for existing applications and a consideration of the performance impact of the protection measures. In this paper we address this problem and propose a solution to enforce privacy over data collections by combining data fragmentation with encryption. The idea behind our approach is to use encryption as an underlying (conveniently available) measure for making data unintelligible, while exploiting fragmentation as a way to break sensitive associations between information. Key words: Privacy, fragmentation, encryption. 1

The impact of privacy requirements in the development of modern applications is increasing very quickly. Many commercial and legal regulations are driving the need to develop reliable solutions for protecting sensitive information whenever it is stored, processed, or communicated to external parties. To this purpose, encryption techniques are currently used in many scenarios where data protection is required since they provide a layer of protection against the disclosure of personal information, which safeguards companies from the costs that may arise from exposing their data to privacy breaches. However, dealing with encrypted data may make query processing more expensive. In this paper, we address these issues by proposing a solution to enforce privacy of data collections that combines data fragmentation with encryption. We model privacy requirements as confidentiality constraints expressing the sensitivity of attributes and their associations. We then use encryption as an underlying (conveniently available) measure for making data unintelligible, while exploiting fragmentation as a way to break sensitive associations among attributes. We formalize the problem of minimizing the impact of fragmentation in terms of number of fragments and their affinity and present two heuristic algorithms for solving such problems. We also discuss

We put forward a novel paradigm for preserving privacy in data outsourcing which departs from encryption. The basic idea behind our proposal is to involve the owner in storing a limited portion of the data, and maintaining all data (either at the owner or at external servers) in the clear. We assume a relational context, where the data to be outsourced is contained in a relational table. We then analyze how the relational table can be fragmented, minimizing the load for the data owner. We propose several metrics and present a general framework capturing all of them, with a corresponding algorithm finding a heuristic solution to a family of NP-hard problems.

The design of distributed databases is an optimization problem requiring solutions to several interrelated problems: data fragmentation, allocation, and local optimization. Each problem can be solved with several different approaches thereby making the distributed database design a very difficult task. Although there is a large body of work on the design of data fragmentation, most of them are either ad hoc solutions or formal solutions for special cases (e. g., binary vertical partitioning). In this paper, we address the problem of n-ary vertical partitioning problem and derive an objective function that generalizes and subsumes earlier work. The objective function derived in this paper is being used for developing heuristic algorithms that can be shown to satisfy the objective function. The objective function is also being used for comparing previously proposed algorithms for vertical partitioning. We first derive an objective function that is suited to distributed transaction proces...

A “sparse ” data set typically has hundreds or even thousands of attributes, but most objects have non-null values for only a small number of these attributes. A popular view about sparse data is that it arises merely as the result of poor schema design. In this paper, we argue that rather than being the result of inept schema design, storing a sparse data set in a single table is the right way to proceed. However, for this to be the case, RDBMSs must provide sparse data management facilities that go beyond the previously studied requirement of storing such data sets efficiently. In particular, an RDBMS must 1) enable users to effectively build ad hoc queries over a very large number of attributes, and 2) support efficient evaluation of these queries over a wide, sparse table. We propose techniques that provide these capabilities, and argue that the single-table approach is a necessary component of selfmanaging database systems because it frees users from a tedious and potentially ineffective schema-design phase when managing sparse data sets.

We report on the design, implementation, and evaluation of a system called Cedar that enables mobile database access with good performance over low-bandwidth networks. This is accomplished without degrading consistency. Cedar exploits the disk storage and processing power of a mobile client to compensate for weak connectivity. Its central organizing principle is that even a stale client replica can be used to reduce data transmission volume from a database server. The reduction is achieved by using content addressable storage to discover and elide commonality between client and server results. This organizing principle allows Cedar to use an optimistic approach to solving the difficult problem of database replica control. For laptop-class clients, our experiments show that Cedar improves the throughput of read-write workloads by 39 % to as much as 224 % while reducing response time by 28 % to as much as 79%.

Although approaches for vertical fragmentation and data allocation have been proposed [13, 16], algorithms for vertical fragmentation and allocation of data and rules in distributed deductive database systems (DDDBSs) are lacking. In this paper, we present different approaches for vertical fragmentation of relations that are referenced by rules and an allocation strategy for rules and fragments in a DDDBS. The potential advantages of the proposed fragmentation and allocation scheme include maximal locality of query evaluation and minimization of communication cost in a distributed system, in addition to the desirable properties of (vertical) fragmentation and rule allocation as discussed in the literature [11, 12]. We also formulate the mathematical interpretation of the proposed vertical fragmentation and allocation algorithms. Keywords: rules, fragmentation, allocation, replication, deductive databases, distributed systems 1 Introduction Deductive database systems enhance the expre...

Given a matrix of values in which the rows correspond to objects and the columns correspond to features of the objects, rearrangement clustering is the problem of rearranging the rows of the matrix such that the sum of the similarities between adjacent rows is maximized. Referred to by various names and reinvented several times, this clustering technique has been extensively used in many fields over the last three decades. In this paper, we point out two critical pitfalls that have been previously overlooked. The first pitfall is deleterious when rearrangement clustering is applied to objects that form natural clusters. The second concerns a similarity metric that is commonly used. We present an algorithm that overcomes these pitfalls. This algorithm is based on a variation of the Traveling Salesman Problem. It offers an extra benefit as it automatically determines cluster boundaries. Using this algorithm, we optimally solve four benchmark problems and a 2,467-gene expression data clustering problem. As expected, our new algorithm identifies better clusters than those found by previous approaches in all five cases. Overall, our results demonstrate the benefits of rectifying the pitfalls and exemplify the usefulness of this clustering technique. Our code is available at our websites.