ExplainIt! -- A declarative root-cause analysis engine for time series data (extended version)

We present ExplainIt!, a declarative, unsupervised root-cause analysis engine that uses time series monitoring data from large complex systems such as data centres. ExplainIt! empowers operators to succinctly specify a large number of causal hypotheses to search for causes of interesting events. ExplainIt! then ranks these hypotheses, reducing the number of causal dependencies from hundreds of thousands to a handful for human understanding. We show how a declarative language, such as SQL, can be effective in declaratively enumerating hypotheses that probe the structure of an unknown probabilistic graphical causal model of the underlying system. Our thesis is that databases are in a unique position to enable users to rapidly explore the possible causal mechanisms in data collected from diverse sources. We empirically demonstrate how ExplainIt! had helped us resolve over 30 performance issues in a commercial product since late 2014, of which we discuss a few cases in detail.Comment: SIGMOD Industry Track 201

Monitoring energy consumption with SIOX

In the face of the growing complexity of HPC systems, their growing energy costs, and the increasing difficulty to run applications efficiently, a number of monitoring tools have been developed during the last years. SIOX is one such endeavor, with a uniquely holistic approach: Not only does it aim to record a certain kind of data, but to make all relevant data available for analysis and optimization. Among other sources, this encompasses data from hardware energy counters and trace data from different hardware/software layers. However, not all data that can be recorded should be recorded. As such, SIOX needs good heuristics to determine when and what data needs to be collected, and the energy consumption can provide an important signal about when the system is in a state that deserves closer attention. In this paper, we show that SIOX can use Likwid to collect and report the energy consumption of applications, and present how this data can be visualized using SIOX’s web-interface. Furthermore, we outline how SIOX can use this information to intelligently adjust the amount of data it collects, allowing it to reduce the monitoring overhead while still providing complete information about critical situations

Nitrate Client Performance Improvement with Cache Implementation

Cílem práce je návrh a implementace výkonnostních vylepšení modulu python-nitrate. Výkonnostní vylepšení jsou založeny na sesbíraných případech užití, které využívají velké množství dat. Za účelem měření dopadu změn v modulu byly implementovány výkonnostní testy. Testování ukázalo, že modul python-nitrate s integrací vylepšení je v některých případech až několikanásobně rychlejší, avšak ve dvou případech může nastat zpomalení. Závěr práce obsahuje diskusi ohledem pokračování prací.The goal of the thesis is to design and implement performance improvements in python-nitrate module. Performance improvements are based on gathered use cases, which use large amount of data and network bandwidth. Performance test suite was implemented in order to measure impact of changes in module. Testing proved, that python-nitrate module with integrated performance improvements is in certain cases several times faster, but also can be slower in two cases. Discussion regarding possible extensions is present in the conclusion.

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Predicting multiple domain queue waiting time via machine learning

This paper describes an implementation of the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology for a demonstrative case of human queue waiting time prediction. We collaborated with a multiple domain (e.g., bank, pharmacies) ticket management service software development company, aiming to study a Machine Learning (ML) approach to estimate queue waiting time. A large multiple domain database was analyzed, which included millions of records related with two time periods (one year, for the modeling experiments; and two year, for a deployment simulation). The data was first preprocessed (including data cleaning and feature engineering tasks) and then modeled by exploring five state-of-the-art ML regression algorithms and four input attribute selections (including newly engineered features). Furthermore, the ML approaches were compared with the estimation method currently adopted by the analyzed company. The computational experiments assumed two main validation procedures, a standard cross-validation and a Rolling Window scheme. Overall, competitive and quality results were obtained by an Automated ML (AutoML) algorithm fed with newly engineered features. Indeed, the proposed AutoML model produces a small error (from 5 to 7 min), while requiring a reasonable computational effort. Finally, an eXplainable Artificial Intelligence (XAI) approach was applied to a trained AutoML model, demonstrating the extraction of useful explanatory knowledge for this domain.This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the R &D Units Project Scope: UIDB/00319/2020 and the project “QOMPASS .: Solução de Gestão de Serviços de Atendimento multi-entidade, multi-serviço e multi-idioma” within the Project Scope NORTE-01-0247-FEDER-038462

An Investigation of the Security Designs of a Structured Query Language (Sql) Database and its Middleware Application and their Secure Implementation Within Thinclient Environments

The Information Portability and Accountability Act (HIPAA) and The SarbanesOxley (SOX) regulations greatly influenced the health care industry regarding the means of securing financial and private data within information and technology. With the introduction of thinclient technologies into medical information systems (IS), data security and regulation compliancy becomes more problematic due to the exposure to the World Wide Web (WWW) and malicious activity. This author explores the best practices of the medical industry and information technology industry for securing electronic data within the thinclient environment at the three levels of architecture: the SQL database, its middleware application, and Web interface. Designing security within the SQL database is not good enough as breaches can occur through unintended consequences during data access within the middleware application design and data exchange design over computer networks. For example, a hospital\u27s medical records, which are routinely exchanged over computer networks, are subject to the audit control an encryption requirements mandated for data security. (Department of, 2008). While there is an overlapping of security techniques within each of the three layers of architectural security design, the use of 18 methodologies greatly enhances the ability to protect electronic information. Due to the variety of IS used within a medical facility, security conscientiousness, consistency of security design, excellent communication between designers, developers and system engineers, and the use of standardized security techniques within each of the three layers of architecture are required