PT7 Web is an annotated Portuguese language Corpus built from samples collected from Sep 2018 to Mar 2020 from seven Portuguese-speaking countries: Angola, Brazil, Portugal, Cape Verde, Guinea-Bissau, Macao e Mozambique. The records were filtered from Common Crawl — a public domain petabyte-scale dataset of webpages in many languages, mixed together in temporal snapshots of the web, monthly available [1]. The Brazilian pages were labeled as the positive class and the others as the negative class (non-Brazillian Portuguese).


Read the 'uncompression_instructions.txt' to know how to extract pt7-corpus.tbz2. The Corpus is structured as a table in 200 parquet files. The user must submit then to a Hadoop, Spark, or similar cluster for processing. The table structure consists of:

|-- label: string
|-- url: string
|-- digest: string
|-- raw: string
|-- tokens: array
| |-- element: string
|-- filtered: array
| |-- element: string

label: 0 for non-Brazillian documents, and 1 for Brazillian documents
url: the source for the original document
digest: a string summary from hash functions over the text
raw: the document in original (raw HTML) format
tokens: the document split into words
filtered: the processed tokens upper three characters, without stopwords

The .tbz2 compressed file may be downloaded by using AWS S3 CLI:

$ aws s3 cp s3://ieee-dataport/open/11618/2804/pt7-corpus.tbz2 <<your_local_or_s3_filesystem>>

[1] G.Wenzek, M.A.Lachaux, A.Conneau,V.Chaudhary, F.Guzman, A.Joulin, E.Grave, arXiv preprint arXiv:1911.00359 (2019).

[2] Rodrigues, J.; Vasconcelos, G.; Maciel, P. Time and cost prediction models for language classification over a large corpus on spark. In: 2020 IEEE Symposium Series on Computational Intelligence (SSCI). [S.l.: s.n.], 2020. p. to appear.


It contains 1 pdf file and 3 excel sheets. The pdf file contains the result graphs  while the excel sheets contain the result data.


Logs from running Monte Carlo simulation as serverless functions on Frankfurt, North Virginia, Tokyo regions of four FaaS systems (AWS, Google, IBM, Alibaba).

Each execution is repeated 5 times (all are warm start). 

The conducted analysis is a part of a submitted manuscript to IEEE TSC. 


The zip file contains several types of datasets.

1. Logs contain details of each execution on all providers / regions. Each column has a self-descriptive title. The first 1000 functions on AWS, 200 on Alibaba, 100 on Google and 100 on IBM are all executed concurrently. The remaining functions are executed once some of the active functions finish due to concurrency limit of the provider.

2. Functions contain the Monte Carlo functions that are executed (in Python).

Based on these logs, we evaluated our xAFCL service along with our new FaaS model and the scheduler. 

3. Makespan<k> contains measured makespan for each set of experiments for scaling factor k. Experiments are denoted as N/r where N is the number of functions that are distributed across the r regions. N=k*r for weak scaling and N=12*r for strong scaling.

4. Regions are ordered in the file xAFCLModelInputs.csv. 

5. Summary presents the achieved average makespan and maximum throughput for each scaling factor k.








Cloud Computing is been the field for interest by the research community and application development industry for few decades now. The ease of development, deployment and management of applications from wide range of computing paradigm and ability to manage the applications over network enabled systems are the biggest selling points of cloud computing. These benefits are materialized using the mechanism called virtualization on cloud computing and in cloud-based Data Centres.


Supplementary material for reviewing process


This dataset contains the execution time of running a total of 3000 functions scattered evenly to three regions: AWS Frankfurt, IBM Frankfurt and IBM Tokyo from University of Innsbruck.

Each execution is repeated three times.


This dataset contains the execution time of running a total of 3000 functions scattered evenly to three regions: AWS Frankfurt, IBM Frankfurt and IBM Tokyo from University of Innsbruck.

The results show how scalable is the execution of no-op function. While the curve of AWS is almost linear starting from 0, IBM curves have a huge jump and then huge period is stable (horizontal).


The dataset (excel document) containst four sheets, one for each region and one for summary, including the diagram. 



This dataset accompanies a paper that discusses the advantages of a 3GPP-compliant service-based architecture platform that demonstrates the concept of cloud-native service orchestration and routing for a media vertical sector application. Cloud-native service orchestration and routing is a complete end-to-end approach that enables virtualisation and management of multiple layers in the OSI model, which provides considerable flexibility and control to achieve delivery of QoS to users in the face of varying demand, at reasonable cost.


A Indústria enfrenta desafios graves e fracassa sem competitividade. Atacando esta problemática, conferiu-se o oferecimento de maior eficiência a processos industriais para promover a produtividade, elevar a qualidade e impulsionar mudanças. A solução desenvolvida incluiu dispositivos com sensores não invasivos, simples de instalar, que contabilizam os itens sendo transportados em linhas de produção.


Os dados foram coletados utilizando o dispositivo IoT da EnergyNow Tecnologias denominado Prodbox™, o qual opera como um equipamento empregado para intensificar a produtividade e apontar maneiras estratégicas de modificar variáveis que interferem na visão de gestão sobre a produção.

O dispositivo utiliza sensores não obstrutivos para contabilizar o número de itens que atravessam a linha de detecção gerada entre o transmissor e o receptor instalados.

Notadamente, os dados coletados são enviados para a nuvem, onde podem, quando integrados a uma plataforma de análise, ser processados para apresentar indicadores de acompanhamento de produtividade. Um sistema inteligente pode processar os dados coletados e apresentar métricas que permitem ao gestor identificar formas de aumentar a produção, bem como etapas que estão prejudicando a produtividade. Além disso, alertas customizados podem ser configurados para prover informação sobre a parada ou inatividade detectada pelo dispositivo.

Os dados gerados através do dispositivo podem ser utilizados para entender melhor variáveis sobre o ritmo de produção e, a partir delas, fomentar projeções de produção, calculando-se a relação entre itens produzidos e período de tempo necessário (segundos, minutos, horas, dias, semanas, etc).


Algumas sugestões sobre abordagens a serem consideradas:

  • Verifique se políticas de aumento de produtividade estão sendo efetivas.

  • Distribuia melhor os funcionários em etapas diferentes de uma linha de produção.

  • Correlacione etapas de produção com variáveis que estejam interferindo na produtividade para resolver problemáticas internas.


Truth discovery techniques, which can obtain accurate aggregation results based on the weighted sensory data of users, are widely adopted in industrial sensing systems. However, there are some privacy matters that cannot be ignored in truth discovery process. While most of the existing privacy preserving truth discovery methods focus on the privacy of sensory data, they may neglect to protect the privacy of another equally important information, the tagged location information.