Robust Private Information Retrieval on Coded Data

We consider the problem of designing PIR scheme on coded data when certain nodes are unresponsive. We provide the construction of $\nu$-robust PIR schemes that can tolerate up to $\nu$ unresponsive nodes. These schemes are adaptive and universally optimal in the sense of achieving (asymptotically) optimal download cost for any number of unresponsive nodes up to $\nu$

Schema architecture and their relationships to transaction processing in distributed database systems

We discuss the different types of schema architectures which could be supported by distributed database systems, making a clear distinction between logical, physical, and federated distribution. We elaborate on the additional mapping information required in architecture based on logical distribution in order to support retrieval as well as update operations. We illustrate the problems in schema integration and data integration in multidatabase systems and discuss their impact on query processing. Finally, we discuss different issues relevant to the cooperation (or noncooperation) of local database systems in a heterogeneous multidatabase system and their relationship to the schema architecture and transaction processing

Generalized quantifiers in distributed databases.

Optimizing queries in a distributed database is quite difficult. This work proposes defining new generalized quantifiers which operate on sets rather than tuples. These quantifiers would allow for easier optimization in a horizontally distributed database. These operators are scalable with respect to both the number of hosts in the environment and the size of the data used

Towards an Efficient Evaluation of General Queries

Database applications often require to evaluate queries containing quantifiers or disjunctions, e.g., for handling general integrity constraints. Existing efficient methods for processing quantifiers depart from the relational model as they rely on non-algebraic procedures. Looking at quantified query evaluation from a new angle, we propose an approach to process quantifiers that makes use of relational algebra operators only. Our approach performs in two phases. The first phase normalizes the queries producing a canonical form. This form permits to improve the translation into relational algebra performed during the second phase. The improved translation relies on a new operator - the complement-join - that generalizes the set difference, on algebraic expressions of universal quantifiers that avoid the expensive division operator in many cases, and on a special processing of disjunctions by means of constrained outer-joins. Our method achieves an efficiency at least comparable with that of previous proposals, better in most cases. Furthermore, it is considerably simpler to implement as it completely relies on relational data structures and operators

Decorrelation of User Defined Function Invocations in Queries

Queries containing user-defined functions (UDFs) are widely used, since they allow queries to be written using a mix of imperative language constructs and SQL, thereby increasing the expressive power of SQL; further, they encourage modularity, and make queries easier to understand. However, not much attention has been paid to their optimization, except for simple UDFs without imperative constructs. Queries invoking UDFs with imperative constructs are executed using iterative invocation of the UDFs, leading to poor performance, especially if the UDF contains queries. Such poor execution has been a major deterrent to the wider usage of complex UDFs

Manycore processing of repeated range queries over massive moving objects observations

The ability to timely process significant amounts of continuously updated spatial data is mandatory for an increasing number of applications. Parallelism enables such applications to face this data-intensive challenge and allows the devised systems to feature low latency and high scalability. In this paper we focus on a specific data-intensive problem, concerning the repeated processing of huge amounts of range queries over massive sets of moving objects, where the spatial extents of queries and objects are continuously modified over time. To tackle this problem and significantly accelerate query processing we devise a hybrid CPU/GPU pipeline that compresses data output and save query processing work. The devised system relies on an ad-hoc spatial index leading to a problem decomposition that results in a set of independent data-parallel tasks. The index is based on a point-region quadtree space decomposition and allows to tackle effectively a broad range of spatial object distributions, even those very skewed. Also, to deal with the architectural peculiarities and limitations of the GPUs, we adopt non-trivial GPU data structures that avoid the need of locked memory accesses and favour coalesced memory accesses, thus enhancing the overall memory throughput. To the best of our knowledge this is the first work that exploits GPUs to efficiently solve repeated range queries over massive sets of continuously moving objects, characterized by highly skewed spatial distributions. In comparison with state-of-the-art CPU-based implementations, our method highlights significant speedups in the order of 14x-20x, depending on the datasets, even when considering very cheap GPUs

Physical Data Independence, Constraints and Optimization with Universal Plans

We present an optimization method and al gorithm designed for three objectives: physi cal data independence, semantic optimization, and generalized tableau minimization. The method relies on generalized forms of chase and backchase with constraints (dependen cies). By using dictionaries (finite functions) in physical schemas we can capture with con straints useful access structures such as indexes, materialized views, source capabilities, access support relations, gmaps, etc. The search space for query plans is defined and enumerated in a novel manner: the chase phase rewrites the original query into a universal plan that integrates all the access structures and alternative pathways that are allowed by appli cable constraints. Then, the backchase phase produces optimal plans by eliminating various combinations of redundancies, again according to constraints. This method is applicable (sound) to a large class of queries, physical access structures, and semantic constraints. We prove that it is in fact complete for path-conjunctive queries and views with complex objects, classes and dictio naries, going beyond previous theoretical work on processing queries using materialized views