A utilização de técnicas compensatórias em drenagem urbana tem se tornado bastante recorrentes diante do processo contínuo de urbanização das últimas décadas. Este trabalho teve o objetivo de avaliar a influência da utilização de cisternas como solução para a atenuação das vazões de escoamento superficial. A área de estudo utilizada foi a bacia do riacho Pajeú, localizada na cidade de Fortaleza, na qual eventos de inundações são comuns durante a quadra chuvosa. Para realizar as simulações, foi utilizado o software Storm Water Management Model (SWMM). Para quantificar os efeitos na redução do pico de vazão causados pela construção de cisternas na bacia, foi simulado um evento de chuva de quatro horas de duração e um período de retorno de 5 anos. Foram avaliados dois cenários: o primeiro, sem a utilização de dispositivos de Low Impact Development (LID), e o segundo, com variadas aplicações de cisternas na bacia. Os resultados das simulações mostraram que o uso de cisternas tem efeitos consideráveis na atenuação do pico de vazão de escoamento, que variam de acordo com a porcentagem de ocupação da bacia. A implantação de cisternas em 50% da bacia pode reduzir em até 64% a vazão máxima de escoamento no ponto exutório.

BACKGROUND: The overall survival of patients with advanced and refractory oesophageal squamous cell carcinoma, mostly aged 65 years and older, is poor. Treatment with PD-1 antibodies showed improved progression-free survival and overall survival. We assessed the safety and efficacy of combined nivolumab and ipilimumab therapy in this population. METHODS: This multicentre, open-label, phase 2 trial done in 32 sites in Germany included patients aged 65 years and older with oesophageal squamous cell carcinoma and disease progression or recurrence following first-line therapy. Patients were treated with nivolumab (240 mg fixed dose once every 2 weeks, intravenously) in the safety run-in phase and continued with nivolumab and ipilimumab (nivolumab 240 mg fixed dose once every 2 weeks and ipilimumab 1 mg/kg once every 6 weeks, intravenously). The primary endpoint was overall survival, which was compared with a historical cohort receiving standard chemotherapy in the intention-to-treat population. This study is registered with ClinicalTrials.gov, NCT03416244. FINDINGS: Between March 2, 2018, and Aug 20, 2020, we screened 75 patients with advanced oesophageal squamous cell carcinoma. We enrolled 66 patients (50 [76%] men and 16 [24%] women; median age 70·5 years [IQR 67·0-76·0]), 44 (67%) of whom received combined nivolumab and ipilimumab therapy and 22 (33%) received nivolumab alone. Median overall survival time at the prespecified data cutoff was 7·2 months (95% CI 5·7-12·4) and significantly higher than in a historical cohort receiving standard chemotherapy (p=0·0063). The most common treatment-related adverse events were fatigue (12 [29%] of 42), nausea (11 [26%]), and diarrhoea (ten [24%]). Grade 3-5 treatment-related adverse events occurred in 13 (20%) of 66 patients. Treatment-related death occurred in one patient with bronchiolitis obliterans while on nivolumab and ipilimumab treatment. INTERPRETATION: Patients aged at least 65 years, with advanced oesophageal squamous cell carcinoma might benefit from combined nivolumab and ipilimumab therapy in second-line treatment. FUNDING: Bristol Myers Squibb.

The Webis MS MARCO Anchor Text 2022 dataset enriches Version 1 and 2 of the document collection of MS MARCO with anchor text extracted from six Common Crawl snapshots. The six Common Crawl snapshots cover the years 2016 to 2021 (between 1.7-3.4 billion documents each). We sampled 1,000 anchor texts for documents with more than 1,000 anchor texts at random and all anchor texts for documents with less than 1,000 anchor texts (this sampling yields that all anchor text is included for 94% of the documents in Version 1 and 97% of documents for Version 2). Overall, the MS MARCO Anchor Text 2022 dataset enriches 1,703,834 documents for Version 1 and 4,821,244 documents for Version 2 with anchor text. Cleaned versions of the MS MARCO Anchor Text 2022 dataset are available in ir_datasets, Zenodo and Hugging Face. The raw dataset with additional information and all metadata for the extracted anchor texts (roughly 100GB) is available on Hugging Face and files.webis.de. The details of the construction of the Webis MS MARCO Anchor Text 2022 dataset are described in the associated paper. If you use this dataset, please cite @InProceedingsfroebe:2022a, address = Berlin Heidelberg New York, author = Maik Fr``obe and Sebastian G''unther and Maximilian Probst and Martin Potthast and Matthias Hagen, booktitle = Advances in Information Retrieval. 44th European Conference on IR Research (ECIR 2022), editor = Matthias Hagen and Suzan Verberne and Craig Macdonald and Christin Seifert and Krisztian Balog and Kjetil Norvrag and Vinay Setty, month = apr, publisher = Springer, series = Lecture Notes in Computer Science, site = Stavanger, Norway, title = The Power of Anchor Text in the Neural Retrieval Era, year = 2022

System logs are valuable sources of information to analyze and diagnose systems behavior. The size of computing systems, and the number of their components, constantly increase. The volume of generated system logs, is in proportion to this increase. Storing system logs for big computing systems, requires high amount of storage capacity. Sensitive data within system logs, raises serious concerns about sharing and publishing them. Using anonymization methods, to cleanse the sensitive data before publishing them, reduces the usability of anonymized system logs for further analysis. After certain level of anonymization, the cleansed system logs, lose their semantic, and only remain useful for some statistical analysis. In this work we address this tradeoff between anonymization and the usefulness of anonymized system logs. Such that, full anonymization of system logs is guaranteed while the minimum required storage is being used, and still the cleansed system logs are usable for general statistical analysis. To achieve this goal, (1) first we need to replace all variables -in each log entry- with defined constant values, (2) then map each log entry into a hash-key via a collision-resistance hash function, (3) and after calculating the frequency of each hash-key, (4) optimize the hash-keys based on their frequency of appearance. Additionally, according to hash-keys frequency, the non-informative hash-keys will be eliminated. Preliminary results of analyzing Taurus system logs via the proposed method, show up to 95% reduction in storage capacity, while the precision of statistical analysis remained unchanged, and the full anonymity is guaranteed.

Biological research is increasingly dependent on analyzing vast amounts of microscopy datasets. Technologies such as Fiji/ImageJ2 and KNIME support knowledge extraction from biological data by providing a large set of configurable algorithms and an intuitive pipeline creation and execution interface. The increasing complexity of required analysis pipelines and the growing amounts of data to be processed nurture the desire to run existing pipelines on HPC (High Performance Computing) systems. Here, we propose a solution to this challenge by presenting a new HPC integration method for KNIME (Konstanz Information Miner) using the UNICORE middleware (Uniform Interface to Computing Resources) and its automated data processing feature. We designed the integration to be efficient in processing large data workloads on the server side. On the client side it is seamless and lightweight to only minimally increase the complexity for the users. We describe our novel approach and evaluate it using an image processing pipeline that could previously not be executed on an HPC system. The evaluation includes a performance study of the induced overhead of the submission process and of the integrated image processing pipeline based on a large amount of data. This demonstrates how our solution enables scientists to transparently benefit from vast HPC resources without the need to migrate existing algorithms and pipelines.

Graph pattern matching is an important and challenging operation on graph data. Typical use cases are related to graph analytics. Since analysts are often non-programmers, a graph system will only gain acceptance, if there is a comprehensible way to declare pattern matching queries. However, respective query languages are currently only supported by graph databases but not by distributed graph processing systems. To enable pattern matching on a large scale, we implemented the declarative graph query language Cypher within the distributed graph analysis platform Gradoop. Using LDBC graph data, we show that our query engine is scalable for operational as well as analytical workloads. The implementation is open-source and easy to extend for further research.

Many big data applications in business and science require the management and analysis of huge amounts of graph data. Suitable systems to manage and to analyze such graph data should meet a number of challenging requirements including support for an expressive graph data model with heterogeneous vertices and edges, powerful query and graph mining capabilities, ease of use as well as high performance and scalability. In this chapter, we survey current system approaches for management and analysis of ``big graph data''. We discuss graph database systems, distributed graph processing systems such as Google Pregel and its variations, and graph dataflow approaches based on Apache Spark and Flink. We further outline a recent research framework called Gradoop that is build on the so-called Extended Property Graph Data Model with dedicated support for analyzing not only single graphs but also collections of graphs. Finally, we discuss current and future research challenges.

Time series data has become a ubiquitous and important data source in many application domains. Most companies and organizations strongly rely on this data for critical tasks like decision-making, planning, predictions, and analytics in general. While all these tasks generally focus on actual data representing organization and business processes, it is also desirable to apply them to alternative scenarios in order to prepare for developments that diverge from expectations or assess the robustness of current strategies. When it comes to the construction of such what-if scenarios, existing tools either focus on scalar data or they address highly specific scenarios. In this work, we propose a generally applicable and easy-to-use method for the generation of what-if scenarios on time series data. Our approach extracts descriptive features of a data set and allows the construction of an alternate version by means of filtering and modification of these features.

Application composition model promises to enable full utilization of extreme scale high performance computing (HPC) systems (aka ExaScale), wherein each composite application consists of several communicative components each of which is tightly coupled with a possibly especial software stack of its own. Heterogeneity of software stacks has encouraged the use of system virtualization technology to isolate executions of components on common shared resources. On the other hand, some components of composite applications, called loosely-coupled components, consist of a set of loosely-coupled CPU-intensive jobs. A given loosely-coupled component runs on a set of virtual machines (VMs), which in turn are distributed on some physical machines. A job scheduler has to assign/reassign jobs to VMs to adaptively cater for the resource provisioning and freeing of newly arrived jobs, and terminated jobs, respectively. Since VMs of several components may share common resources of a certain physical machine, and given that the job scheduler of each component is totally unaware of virtualization, the job scheduler of a loosely-coupled component does not know the status of physical machines. So, reconfiguration of the job scheduler’s parameters at runtime can give the true state of the physical machine based on which, the job scheduler can assign/reassign jobs to more suitable VMs. This paper presents a combination of ASSIGN-ROUTE online job scheduling and a reconfiguration technique allowing a given loosely-coupled component to balance its resource usage load, and thus improve the scaled execution of its loosely-coupled jobs. We prove that this technique reaches the load imbalance which is near to the optimal load imbalance for online deterministic unrelated parallel machine makespan minimization scheduling. We also show that the results of our experiments, support the theoretical achievements.

Considering the cost effectiveness, elasticity, and flexibility of virtualized cloud environments, porting HPC applications to those environments and executing them within these settings is becoming more and more popular. For this purpose, traditional HPC applications have to be redesigned as cloud applications. An analysis of the performance of the redesigned applications within the cloud environment is in-dispensable, if the applications should be efficiently executed in the cloud environment.This paper proposes a workflow for the integral performance analysis of cloud applications within a cloud environment using monitoring and tracing techniques. For this, collectd acts as a lightweight monitoring daemon for recording performance data from outside of the applications, Score-P as a profiling and tracing tool for recording the performance data from the inside. Thus, this workflow will help in answering the question "How and why an application behaves like this within the cloud environment?".In order to show the usability of the proposed workflow, a parallel client server application was selected and adapted for the execution in a private OpenStack cloud. Performance measurements of the example running in the cloud environment could be successfully done according to the proposed workflow. In particular, performance metrics from both the outside and the inside of the application could be obtained to analyze and evaluate the performance of the application in detail.

In a project called Ä Library of a Billion Words" we needed an implementation of the CTS protocol that is capable of handling a text collection containing at least 1 billion words. Because the existing solutions did not work for this scale or were still in development I started an implementation of the CTS protocol using methods that MySQL provides. Last year we published a paper that introduced a prototype with the core functionalities without being compliant with the specifications of CTS (Tiepmar et al., 2013). The purpose of this paper is to describe and evaluate the MySQL based implementation now that it is fulfilling the specifications version 5.0 rc.1 and mark it as finished and ready to use. Further information, online instances of CTS for all described datasets and binaries can be accessed via the projects website.

Abstract argumentation frameworks are by now a major research area in knowledge representation and reasoning. Various aspects of AFs have been extensively studied over the last 25 years. Contributing to understanding the expressive power of AFs, researchers found lower and upper bounds for the maximal number of extensions, that is, acceptable points of view, in AFs. One of the classical and most important concepts in AFs are so-called complete extensions. Surprisingly, the exact bound for the maximal number of complete extensions in an AF has not yet been formally established, although there is a reasonable conjecture tracing back at least to 2015. Recently the notion of modularization was introduced and it was shown that this concept plays a key role for the understanding of relations between semantics as well as intrinsic properties. In this paper, we will use this property to give a formal proof of the conjecture regarding complete semantics.

Large interactive displays are increasingly important and a relevant research topic, and several studies have focused on wall interaction. However, in many cases, thorough user studies currently require time-consuming video analysis and coding. We present the Group Interaction Analysis Toolkit GIAnT, which provides a rich set of visualizations supporting investigation of multi-user interaction at large display walls. GIAnT focuses on visualizing time periods, making it possible to gain overview-level insights quickly. The toolkit is designed to be extensible and features several carefully crafted visualizations: A novel timeline visualization shows movement in front of the wall over time, a wall visualization shows interactions on the wall and gaze data, and a floor visualization displays user positions. In addition, GIAnT shows the captured video stream along with basic statistics. We validate our tool by analyzing how it supports investigating major research topics and by practical use in evaluating a cooperative game.

Wikidata, now a decade old, is the largest public knowledge graph, with data on more than 100 million concepts contributed by over 560,000 editors. It is widely used in applications and research. At its launch in late 2012, however, it was little more than a hopeful new Wikimedia project, with no content, almost no community, and a severely restricted platform. Seven years earlier still, in 2005, it was merely a rough idea of a few PhD students, a conceptual nucleus that had yet to pick up many important influences from others to turn into what is now called Wikidata. In this paper, we try to recount this remarkable journey, and we review what has been accomplished, what has been given up on, and what is yet left to do for the future.

This study aimed to understand the perception of drought among farmers, in order to support decision-making in the water allocation process. This study was carried out in the Tabuleiro de Russas irrigated perimeter, in northeast Brazil, over the drought period of 2012–2018. Two analyses were conducted: (i) drought characterization, using the Standardized Precipitation Index (SPI) based on drought duration and frequency criteria; and (ii) analysis of farmers’ perceptions of drought via selection of explanatory variables using the Random Forest (RF) and the Decision Tree (DT) methods. The 2012–2018 drought period was defined as a meteorological phenomenon by local farmers; however, an SPI evaluation indicated that the drought was of a hydrological nature. According to the RF analysis, four of the nine study variables were more statistically important than the others in influencing farmers’ perception of drought: number of cultivated land plots, farmer’s age, years of experience in the agriculture sector, and education level. These results were confirmed using DT analysis. Understanding the relationship between these variables and farmers’ perception of drought could aid in the development of an adaptation strategy to water deficit scenarios. Farmers’ perception can be beneficial in reducing conflicts, adopting proactive management practices, and developing a holistic and efficient early warning drought system.