away from the underlying programming language in favour of using graphical means of programming, such as wizards and statecharts. Platforms. Closed frameworks such as DESIRE, strongly based on a platform, provide more complete solutions than others such as Jadex or TuCSoN. They usually offer an agent architecture and a system model, very useful for novel developers, together with the communication infrasA SURVEY OF LANGUAGES AND PLATFORMS. . . Informatica 30 (2006) 33–44 41 tructure and a range of robust services, such as directory facilitators, agent management services, and monitoring facilities. As a drawback, closed frameworks limit the development. For example, the design approach of the framework may not fit certain domain problems. Perhaps that is the reason why most researchers tend to use more open solutions. Currently, the most popular solution is to use JADE as underlying agent infrastructure combined with some other (higher-level) approach to program the agents’ behaviour. When dealing with more general frameworks (rather than tied to a platform), their use (i.e., defining the agents that will run within it, together with the required services and resources) should be automated as much as possible, in part to free the developer from low-level details (e.g. location of the configuration files, their concrete syntax, etc.). Despite this, few existing frameworks have IDE support. Concerning the paradigm of communication used, there are several on offer, often being an important issue when choosing which framework to adopt. TuCSoN is representative of tuple-centred communication, JADE of message passing, and DESIRE of data flow among processes. The various approaches mentioned along this survey indicate that there is still much work to be done. Among the major challenges faced by this research community are: – The conception and development of specialised debugging tools, in particular for cognitive agent languages; – The integration of agent tools into existing IDEs, rather than starting from scratch; – The separation of MAS frameworks from agent platforms, so that each framework can be used for deploying systems on a variety of platforms. – The dissemination of the MAS programming paradigm, so that programmers have a better understanding of its foundations as well as practical characteristics. We believe that the recent developments surveyed here show a lively interest in this area of research. Despite the large number of open issues and challenges, we expect that the experience gathered in developing MAS with these tools will take us closer to a more mature programming paradigm. Arguably, this is one of the few concrete ways for allowing wider audiences to use in practice, and in a systematic way, the various techniques that the MAS research community has developed over the last two decades.

/pdf/a-heading-maintaining-oriented-compression-algorithm-for-gps-n61mo0y08w.pdf

A Heading Maintaining Oriented Compression Algorithm for GPS Trajectory Data

Cloud computing is an emerging paradigm that provides computing, communication and storage resources as a service over a network. Communication resources often become a bottleneck in service provisioning for many cloud applications. Therefore, data replication which brings data (e.g., databases) closer to data consumers (e.g., cloud applications) is seen as a promising solution. It allows minimizing network delays and bandwidth usage. In this paper we study data replication in cloud computing data centers. Unlike other approaches available in the literature, we consider both energy efficiency and bandwidth consumption of the system. This is in addition to the improved quality of service QoS obtained as a result of the reduced communication delays. The evaluation results, obtained from both mathematical model and extensive simulations, help to unveil performance and energy efficiency tradeoffs as well as guide the design of future data replication solutions.

/pdf/energy-efficient-data-replication-in-cloud-computing-1ijrfbbigx.pdf

Energy-efficient data replication in cloud computing datacenters

We live in an increasingly interconnected world, with many organizations operating across countries or even continents. To serve their global user base, organizations are replacing their legacy DBMSs with cloud-based systems capable of scaling OLTP workloads to millions of users. CockroachDB is a scalable SQL DBMS that was built from the ground up to support these global OLTP workloads while maintaining high availability and strong consistency. Just like its namesake, CockroachDB is resilient to disasters through replication and automatic recovery mechanisms. This paper presents the design of CockroachDB and its novel transaction model that supports consistent geo-distributed transactions on commodity hardware. We describe how CockroachDB replicates and distributes data to achieve fault tolerance and high performance, as well as how its distributed SQL layer automatically scales with the size of the database cluster while providing the standard SQL interface that users expect. Finally, we present a comprehensive performance evaluation and share a couple of case studies of CockroachDB users. We conclude by describing lessons learned while building CockroachDB over the last five years.

https://dl.acm.org/doi/pdf/10.1145/3318464.3386134

CockroachDB: The Resilient Geo-Distributed SQL Database

GPS-equipped mobile devices such as smart phones and in-car navigation units are collecting enormous amounts of spatial and temporal information that traces a moving object's path. The exponential increase in the amount of such trajectory data has caused three major problems. First, transmission of large amounts of data is expensive and time-consuming. Second, queries on large amounts of trajectory data require computationally expensive operations to extract useful patterns and information. Third, GPS trajectories often contain large amounts of redundant data that waste storage and cause increased disk I/O time. These issues can be addressed by algorithms that reduce the size of trajectory data. A key requirement for these algorithms is to minimize the loss of information essential to location-based applications. This paper presents a new compression method called SQUISH-E (Spatial QUalIty Simplification Heuristic - Extended) that provides improved run-time performance and usability. A comprehensive comparison of SQUISH-E with other algorithms is carried out through an empirical study across three types of real-world datasets and a variety of error metrics.

https://www.utrc2.org/sites/default/files/pubs/GPS-Traces-Jouranl-Article.pdf

Compression of trajectory data: a comprehensive evaluation and new approach

Web application testing: A systematic literature review

GPS-equipped mobile devices such as smart phones and in-car navigation units are collecting enormous amounts spatial and temporal information that traces a moving object's path. The popularity of these devices has led to an exponential increase in the amount of GPS trajectory data generated. The size of this data makes it difficult to transmit it over a mobile network and to analyze it to extract useful patterns. Numerous compression algorithms have been proposed to reduce the size of trajectory data sets; however these methods often lose important information essential to location-based applications such as object's position, time and speed. This paper describes the Spatial QUalIty Simplification Heuristic (SQUISH) method that demonstrates improved performance when compressing up to roughly 10% of the original data size, and preserves speed information at a much higher accuracy under aggressive compression. Performance is evaluated by comparison with three competing trajectory compression algorithms: Uniform Sampling, Douglas-Peucker and Dead Reckoning.

SQUISH: an online approach for GPS trajectory compression

Recent years have seen rapid growth of online data storage and computing services at various locations around the world. In wide area applications, data can be replicated at multiple locations to serve users with lower latency and higher availability. This paper presents an approach that achieves both fast and highly available data access through periodic migration of data replicas. Such migration strives to maximize a user-defined objective function that incorporates data access delay and availability into a single utility value. To efficiently estimate data access delay and availability for any feasible replica placement, this approach maintains a small data structure that summarizes recent accesses to data replicas. This paper demonstrates, based on an evaluation study, the effectiveness of the developed technique and concludes with plans for future research.

Wide area placement of data replicas for fast and highly available data access

There has been proliferation of data centers that provide both computation and storage resources at diverse geographic locations. In a variety of wide area applications, data can be replicated to serve users with lower latency. This paper presents a technique that can effectively reduce the overall data access delay through gradual migration of data replicas. In contrast to previous solutions that either randomly select replica locations or process a large log of past data accesses, this new technique maintains only a small, decentralized summary of recent data accesses while achieving near optimal performance. This paper also includes an evaluation study that substantiates the effectiveness of the developed technique, and plans for extending the current research outcomes.

Towards Optimal Data Replication Across Data Centers

A Retrospective Approach for Accurate Network Latency Prediction

iFlow is a replication-based system that can achieve both fast and reliable processing of high volume data streams on the Internet scale. iFlow uses a low degree of replication in conjunction with detouring techniques to overcome network congestion and outages. Computation over iFlow can be expressed as a graph of operators. To cope with varying system conditions these operators continually migrate in a manner that improves performance and availability at the same time.In this paper, we first provide an overview of our iFlow system. Next, we detail how our detouring technique works in the face of network failures to provide high availability for time critical applications. The paper also includes a description of our implementation and preliminary evaluation results demonstrating that iFlow outperforms previous solutions with less overhead. Finally, the paper concludes with our plans for enhancing replication and detouring capabilities.

/pdf/detouring-and-replication-for-fast-and-reliable-internet-1mazuhpd45.pdf

Detouring and replication for fast and reliable internet-scale stream processing

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Fan Ping

Author Tools

Chat about Author