Monografias em Ciência da Computação
Departmento de Informática
Pontifícia Universidade Católica do Rio de Janeiro - PUC-Rio
Rio de Janeiro - Brazil
This file contains a list of the technical reports of the Departmento de Informática, Pontifícia Universidade Católica do Janeiro - PUC-Rio, Brazil, which are published in our series Monografias em Ciência da Computação (ISSN 0103-9741), edited by Prof. Carlos Lucena. Please note that the reports not available for download are available in their print format and can be obtained via the e-mail below.
For any questions, requests or suggestions, please contact:
Rosane Castilho firstname.lastname@example.org
Last update: 21/OCTOBER/2014
LIMA, E.S.; FURTADO, A.L. Handling Google snippets with SWI-Prolog. 25 p. Eng. E-mail: email@example.com
Abstract: We have designed – and implemented in a preliminary version – a tool, named LOG-SNIP, for capturing snippets while performing Google searches for Web resources pertaining to a domain of interest, based on keywords adequate to delimit the domain. The snippets are decomposed into separate fields: name, date, url, info. A kws field is added by extracting resource-specific keywords from the name and info fields. Under the form of a five-field frame structure, the chosen snippets can then be recorded as Prolog clauses, to be subsequently used for all sorts of research purposes. Of particular value is the ability to employ the sets of resource-specific keywords to perform comparisons among the located domain resources. To present one possible application, we implemented a module that translates the stored clauses into the clauses required to run our previously created KWGPS tool.
ARAÚJO, T.P.; CERQUEIRA, R.; STAA, A.v. Supporting failure diagnosis with logs containing meta-information annotations. 21 p. Eng. E-mail: firstname.lastname@example.org
Abstract: Many failures in distributed systems are hard to diagnose due to the difficulty to collect, organize and relate information about their overall state and behavior. When a failure is detected while testing or using such a system, it is often quite difficult to infer the system’s state and the performed operations that have some connection with the cause of the problem. Traditional debugging techniques usually do not apply, and when they do, they are often not effective. The problem is aggravated when failures are detected at run-time, since it is usually impossible to replicate the sequence of execution that caused the failure. This work presents a diagnosing mechanism based on logs of events annotated with contextual information, allowing a specialized visualization tool to filter them according to the maintainer’s needs. We have successfully applied this mechanism to a deployed system composed of mobile applications, web servers and cloud services. The effort to instrument was low, approximately 14% of the development effort. We also conducted a proof of concept with users, which showed that the cost to diagnose the cause of the failures can be dramatically reduced using this approach.
RORIZ JUNIOR, M.; SCHNEIDER, R.; ENDLER, M.; SILVA, F.S. An On-line algorithm for cluster detection of mobile nodes through complex event processing. 19 p. Eng. E-mail: email@example.com
Abstract: The concentration (cluster) of mobile entities in a certain region, e.g., a mass street protest, a rock concert, or a traffic jam, is an information that can benefit several distributed applications. Nevertheless, cluster detection in on-line scenarios is a challenging task, primary because it requires efficient and complex algorithms to handle the high volume of position data in a timely manner. To address this issue, in this paper, we proposed dg2cep, an on-line algorithm inspired by data mining algorithms and based on Complex Event Processing stream-oriented concepts for on-line detection of such clusters. Our experiments indicates that dg2cep can rapidly detected, in less than few seconds, the cluster formation and dispersion. In addition, the required time to detect such clusters scale linearly with the number of nodes. Finally, regarding accuracy, the experiments shows that the cluster detected by dg2cep presented a very high degree of similarity with the classic data mining clustering algorithm.
Abstract:Few studies have investigated and proposed a middleware solution for the Internet of Mobile Things (IoMT), where the smart things (Smart Objects) can be moved, or else can move autonomously, and yet remain accessible and controllable remotely from any other computer over the Internet. Examples of mobile Smart Objects include vehicles of any nature, wearable devices, smart watches, sensor tags, mobile robots, Unmanned Aerial Vehicles (UAVs), i.e., any mobile thing with embedded sensors and/or actuators. In this context of general and unrestricted mobility of Smart Objects, the main challenge is to ensure endured connectivity and discovery of Smart Objects, as well as the efficient scalable and reliable remote access to its sensors and actuators. This paper describes the concept a Mobile Hub as a key enabler of the Internet of Mobile Things, its design and an initial implementation of the concept for Android and a single WPAN technology: Bluetooth Low Energy. The M-Hub is the natural extension of the Scalable Data Distribution Layer (SDDL), a mobile communication middleware developed by our group that adopts a mobile-cloud architecture and provides scalable mobile-mobile communication and processing capabilities. Preliminary experiments have shown that our implementation for BLE delivers good mobility responsiveness and that the concept is suitable for applications which have to deal with the mobility of Smart Objects/ things.
Abstract:To tell a story, the storyteller uses all his/her skills to entertain an audience. This task not only relies on the act of telling a story, but also on the ability to understand reactions of the audience during the telling of the story. A well-trained storyteller knows whether the audience is bored or enjoying the show just by observing the spectators and adapts the story to please the audience. In this work, we propose a methodology to create tailored stories to an audience based on personality traits and preferences of each individual. As an audience may be composed of individuals with similar or mixed preferences, it is necessary to consider a middle ground solution based on the individual options. In addition, individuals may have some kind of relationship with others that influence their decisions. The proposed model addresses all steps in the quest to please the audience. It infers what the preferences are, computes the scenes reward for all individuals, estimates their choices independently and in group, and allows Interactive Storytelling systems to find the story that maximizes the expected audience reward.