Distributed architecture is widely used for storing and processing Big Data. Operations on Big Data need first, locating the required data blocks and then, reading them. Data can be located in different types of memories in particular, cache memory, main memory, and secondary memory. Reading data from secondary memory to process Big Data jobs is not an ideal approach especially for high performance applications because, accessing data in secondary devices can be slow for processors. In addition, fetching data from main memory is time consuming due to limited I/O bandwidth. These system level issues are barriers for optimizing performance of Big Data applications. Simply put, for optimizing the application performance, it is not sufficient to have efficient algorithms only, an efficient architecture is needed to provide faster data access by the processors. The need for such an architecture has been documented in the literature, however, the state of the art is still missing an efficient architecture. This paper develops a promising architecture which caches data in main memory. It essentially transforms a main memory into a attraction memory which enables high-speed data access. Also, it enables automatic migration of data blocks and computations across the nodes contained in the clusters. It offers an exchange protocol for fast transfer of data blocks between the different physical nodes and speeds up job processing. The proposed architecture combines the power of Cache-Only Memory Architecture (COMA) and the structural principle of Hadoop.

Formal calculi of record structures have recently been a focus of active research. However, scarcely anyone has studied formally the dual notion—i.e., argument-passing to functions by keywords, and its harmonization with currying. We have. Recently, we introduced the label-selective λ-calculus, a conservative extension of λ-calculus that uses a labeling of abstractions and applications to perform unordered currying. In other words, it enables some form of commutation between arguments. This improves program legibility, thanks to the presence of labels, and efficiency, thanks to argument commuting. In this paper, we propose a simply typed version of the calculus, then extend it to one with ML-like polymorphic types. For the latter calculus, we establish the existence of principal types and we give an algorithm to compute them. Thanks to the fact that label-selective λ-calculus is a conservative extension of λ-calculus by adding numeric labels to stand for argument positions, its polymorphic typing provides us with a keyword argument-passing extension of ML obviating the need of records. In this context, conventional ML syntax can be seen as a restriction of the more general keyword-oriented syntax limited to using only implicit positions instead of keywords.

LIFE is a programming language proposing to integrate logic programming, functional programming, and object-oriented programming. It replaces first-order terms with ψ-terms, data structures that allow computing with partial information. These are approximation structures denoting sets of values. LIFE further enriches the expressiveness of ψ-terms with functional dependency constraints. We must explain the meaning and use of functions in LIFE declaratively, as solving partial information constraints. These constraints do not attempt to generate their solutions but behave as demons filtering out anything else. In this manner, LIFE functions act as declarative coroutines. We need to show that the ψ-term's approximation semantics is congruent with an operational semantics viewing functional reduction as an effective enforcing of passive constraints. In this article, we develop a general formal framework for entailment and disentailment of constraints based on a technique called relative simplification. We study its operational and semantical properties, and we use it to account for functional application over ψ-terms in LIFE.

Lattice operations such as greatest lower bound (GLB), least upper bound (LUB), and relative complementation (BUTNOT) are becoming more and more important in programming languages supporting object inheritance. We present a general technique for the efficient implementation of such operations based on an encoding method. The effect of the encoding is to plunge the given ordering into a boolean lattice of binary words, leading to an almost constant time complexity of the lattice operations. A first method is described based on a transitive closure approach. Then a more space-efficient method minimizing code-word length is described. Finally a powerful grouping technique called modulation is presented, which drastically reduces code space while keeping all three lattice operations highly efficient. This technique takes into account idiosyncrasies of the topology of the poset being encoded that are quite likely to occur in practice. All methods are formally justified. We see this work as an original contribution towards using semantic (vz., in this case, taxonomic) information in the engineering pragmatics of storage and retrieval of (vz., partially or quasi-ordered) information.

This report discusses the implementation of a knowledge base for a library information system. It is done using a typed logic programming language—LOGIN—where type inheritance is built in. The knowledge base is structured in a hierarchical taxonomy of library object classes where each class is represented in a FRAME style knowledge structure and inherits the properties of its parents, and where infrastructural inference rules have been established through typed Horn clauses. Also in this document, some programming techniques aimed at using the power of inheritance as taxonomic inference are discussed.

An elaboration of the Prolog language is described in which the notion of first-order term is replaced by a more general one. This extended form of terms allows the integration of inheritance---an IS-A taxonomy---directly into the unification process rather than indirectly through the resolution-based inference mechanism of Prolog. This results in more efficient computations and enhanced language expressiveness. The language thus obtained, called LOGIN, subsumes Prolog, in the sense that conventional Prolog programs are equally well executed by LOGIN.