MERCURY: Distributed Incremental Attribute Grammar Evaluation

This technical report consists of the two most recent papers from the MERCURY project Multiuser, Distributed Language-Based Environments explains the application of incremental attribute grammar evaluation algorithms to generation of distributed programming environments and describes the implementation of the MERCURY system. Version and Configuration Control in Distributed Language-Based Environments presents new algorithms that permit MERCURY to support multiple versions and configurations of modules and to more efficiently propagate changes to aggregate attributes

Incremental Evaluation of Ordered Attribute Grammars for Asynchronous Subtree Replacements

Incremental algorithms for evaluating attribute grammars (AGs) have been extensively studied in recent years, primarily because of their application in language-based environments. Ordered attribute grammars are a subclass of AGs for which efficient evaluators can be constructed. Previous incremental algorithms for ordered attribute grammars only allowed one modification to the program at a time, requiring attribute evaluation due to one change to quiesce before another one due to a second change can start. This article presents new incremental evaluation algorithms for ordered attribute grammars that can handle asynchronous program modifications in an optimal manner. Support for asynchronous changes is necessary in environments for multiple users, where different programmers may be making changes to different parts of the program simultaneously. The key to the optimality of the algorithm is an ordering of the attribute evaluations so that an attribute affected by more than one change will only be evaluated once if the changes happen concurrently

Incremental Dynamic Semantics for Language-Based Programming Environments

Attribute grammars are a formal notation for expressing the static semantics of programming languages — those properties that can be derived from inspection of the program text. Attribute grammars have become popular as a mechanism for generating language-based programming environments that incrementally perform symbol resolution, type checking, code generation and derivation of other static semantic properties as the program is modified. However, attribute grammars are not suitable for expressing dynamic semantics — those properties that reflect the history of program execution and/or user interactions with the programming environment. This article presents action equations, an extension of attribute grammars suitable for specifying the static and the dynamic semantics of programming languages. It describes how action equations can be used to generate language-based programming environments that incrementally derive static and dynamic properties as the user modifies and debugs the program

GAEA Action Equations Paradigm

This technical report consists of two papers describing the GAEA action equations paradigm. Incremental Dynamic Semantics for Language-based Programming Environments explains why attribute grammars are not suitable for expressing dynamic semantics and presents action equations, an extension of attribute grammars suitable for specifying the static and the dynamic semantics of programming languages. It describes how action equations can be used to generate language-based programming environments that incrementally derive static and dynamic properties as the user modifies and debugs the program. Rapid Prototyping of Concurrent Programming Languages extends this technology to a concurrent framework. It describes an (unimplemented) system that generates a parallel interpreter for the language and provides runtime support for the synchronization primitives and other facilities in the language

Factors shaping the evolution of electronic documentation systems

The main goal is to prepare the space station technical and managerial structure for likely changes in the creation, capture, transfer, and utilization of knowledge. By anticipating advances, the design of Space Station Project (SSP) information systems can be tailored to facilitate a progression of increasingly sophisticated strategies as the space station evolves. Future generations of advanced information systems will use increases in power to deliver environmentally meaningful, contextually targeted, interconnected data (knowledge). The concept of a Knowledge Base Management System is emerging when the problem is focused on how information systems can perform such a conversion of raw data. Such a system would include traditional management functions for large space databases. Added artificial intelligence features might encompass co-existing knowledge representation schemes; effective control structures for deductive, plausible, and inductive reasoning; means for knowledge acquisition, refinement, and validation; explanation facilities; and dynamic human intervention. The major areas covered include: alternative knowledge representation approaches; advanced user interface capabilities; computer-supported cooperative work; the evolution of information system hardware; standardization, compatibility, and connectivity; and organizational impacts of information intensive environments

REX: Recursive, Delta-Based Data-Centric Computation

In today's Web and social network environments, query workloads include ad hoc and OLAP queries, as well as iterative algorithms that analyze data relationships (e.g., link analysis, clustering, learning). Modern DBMSs support ad hoc and OLAP queries, but most are not robust enough to scale to large clusters. Conversely, "cloud" platforms like MapReduce execute chains of batch tasks across clusters in a fault tolerant way, but have too much overhead to support ad hoc queries. Moreover, both classes of platform incur significant overhead in executing iterative data analysis algorithms. Most such iterative algorithms repeatedly refine portions of their answers, until some convergence criterion is reached. However, general cloud platforms typically must reprocess all data in each step. DBMSs that support recursive SQL are more efficient in that they propagate only the changes in each step -- but they still accumulate each iteration's state, even if it is no longer useful. User-defined functions are also typically harder to write for DBMSs than for cloud platforms. We seek to unify the strengths of both styles of platforms, with a focus on supporting iterative computations in which changes, in the form of deltas, are propagated from iteration to iteration, and state is efficiently updated in an extensible way. We present a programming model oriented around deltas, describe how we execute and optimize such programs in our REX runtime system, and validate that our platform also handles failures gracefully. We experimentally validate our techniques, and show speedups over the competing methods ranging from 2.5 to nearly 100 times.Comment: VLDB201

Data Auditing and Security in Cloud Computing: Issues, Challenges and Future Directions

Cloud computing is one of the significant development that utilizes progressive computational power and upgrades data distribution and data storing facilities. With cloud information services, it is essential for information to be saved in the cloud and also distributed across numerous customers. Cloud information repository is involved with issues of information integrity, data security and information access by unapproved users. Hence, an autonomous reviewing and auditing facility is necessary to guarantee that the information is effectively accommodated and used in the cloud. In this paper, a comprehensive survey on the state-of-art techniques in data auditing and security are discussed. Challenging problems in information repository auditing and security are presented. Finally, directions for future research in data auditing and security have been discussed

Data auditing and security in cloud computing: issues, challenges and future directions

Cloud computing is one of the significant development that utilizes progressive computational power and upgrades data distribution and data storing facilities. With cloud information services, it is essential for information to be saved in the cloud and also distributed across numerous customers. Cloud information repository is involved with issues of information integrity, data security and information access by unapproved users. Hence, an autonomous reviewing and auditing facility is necessary to guarantee that the information is effectively accommodated and used in the cloud. In this paper, a comprehensive survey on the state-of-art techniques in data auditing and security are discussed. Challenging problems in information repository auditing and security are presented. Finally, directions for future research in data auditing and security have been discusse

Locality-aware scientific workflow engine for fast-evolving spatiotemporal sensor data, A

2017 Spring.Includes bibliographical references.Discerning knowledge from voluminous data involves a series of data manipulation steps. Scientists typically compose and execute workflows for these steps using scientific workflow management systems (SWfMSs). SWfMSs have been developed for several research communities including but not limited to bioinformatics, biology, astronomy, computational science, and physics. Parallel execution of workflows has been widely employed in SWfMSs by exploiting the storage and computing resources of grid and cloud services. However, none of these systems have been tailored for the needs of spatiotemporal analytics on real-time sensor data with high arrival rates. This thesis demonstrates the development and evaluation of a target-oriented workflow model that enables a user to specify dependencies among the workflow components, including data availability. The underlying spatiotemporal data dispersion and indexing scheme provides fast data search and retrieval to plan and execute computations comprising the workflow. This work includes a scheduling algorithm that targets minimizing data movement across machines while ensuring fair and efficient resource allocation among multiple users. The study includes empirical evaluations performed on the Google cloud