Online event detection in streaming time series is a critical task with applications across various domains. For example, the right-on-time event detection for control systems is a key for correctly addressing the issues related to the events. However, events may not be identified right after their occurrence. Depending on the monitoring solution, a time difference may exist between the event’s occurrence and detection. This problem raises research questions regarding the study of such a temporal gap. The paper introduces novel metrics (detection probability and detection lag) to address these questions. It explores the impact of configurable batches on detection performance. The experimental evaluation of diverse datasets reveals nuanced insights into the interplay between batch parameters, detection accuracy, and computational performance.

Over recent years, several sophisticated Process-Aware Information Systems (PAIS) have been proposed for managing business processes and automating large-scale scientific (e-Science) processes. Much of this success is due to their ability to provide generic functionality for modeling, execution and monitoring processes. These functionalities work well when process execution follows a well-behaved path towards achieving the models objectives. However, exceptions and anomalous situations that fall outside of the well-behaved execution path still pose a significant challenge to PAIS. The treatment for such exceptions usually involves interventions in systems by human operators, which result in significant additional cost for businesses. In this paper, we introduce a cost-aware recovery composition method that is able to find and follow recovery paths that reduce the cost of exception handling. From a practical point of view, our proposal reduces complexity and the need for manual interventions to handle exceptions. Finally, the feasibility of recovery mechanism is discussed from its implementation into WED-flow framework.

With billions of active social media accounts and millions of live video cameras, live new big data offer many opportunities for smart applications. However, the main consumers of the new big data have been humans. We envision the research on live knowledge , to automatically acquire real-time, validated, and actionable information. Live knowledge presents two significant and diverging technical challenges: big noise and concept drift. We describe the EBKA (evidence-based knowledge acquisition) approach, illustrated by the LITMUS landslide information system. LITMUS achieves both high accuracy and wide coverage, demonstrating the feasibility and promise of EBKA approach to achieve live knowledge.

The University of São Paulo is the largest public university in Brasil, with 11 campuses where the campus in the city of São Paulo alone has an area of more than 3 million square meters. Security in the university is an issue that is being prioritized in the past years. A mobile app was introduced two years ago with which users in any campus can report security and maintenance events. Security events are monitored by the Campus Security Guard that has the capacity to dispatch agents immediately upon report of an event by the app. Last year an Electronic Monitoring System (EMS) composed of more than 300 cameras was deployed in the campus of the city of São Paulo together with analytics software that greatly enhances its functionality. Integrating the mobile app and the EMS is a great challenge that is being addressed in this paper. Here we present a description of both systems: the mobile application and the EMS. Then we present the Artificial Intelligence Surveillance system (AISUSP), which integrates both other systems using machine learning for automated scene analysis, an impossible task for human operators in an environment with more than 300 cameras. The ongoing result will lead to the prediction of emergency situations using a historical data base and making intelligent decisions related to the university's users safety.

During the donation process, blood donors are screened for their hemoglobin or hematocrit level to protect them from developing anemia. Nevertheless, there is no standard procedure to predict anemia development after blood donation. The São Paulo Blood Center is responsible for maintaining a database with information on each donation. However, this database does not have good quality, and consequently, it is difficult to establish systematic analyses using the donation database without previously validating the data. To provide better quality donation data, this paper presents a provenance description based on classification criteria defined by specialists. More concretely, this paper answers the following main question: is there a connection between blood donation and a decrease in hematocrit levels?" This question was addressed to prevent undesirable outcomes to blood donors. In this paper, we show that it is possible to provide detailed investigations to answer this main question using the data description without the need to impose changes in the current database system structure sponsored by the São Paulo Blood Center.

BACKGROUND: In this study, clustering was performed using a bitmap representation of HIV reverse transcriptase and protease sequences, to produce an unsupervised classification of HIV sequences. The classification will aid our understanding of the interactions between mutations and drug resistance. 10,229 HIV genomic sequences from the protease and reverse transcriptase regions of the pol gene and antiretroviral resistant related mutations represented in an 82-dimensional binary vector space were analyzed. RESULTS: A new cluster representation was proposed using an image inspired by microarray data, such that the rows in the image represented the protein sequences from the genotype data and the columns represented presence or absence of mutations in each protein position.The visualization of the clusters showed that some mutations frequently occur together and are probably related to an epistatic phenomenon. CONCLUSION: We described a methodology based on the application of a pattern recognition algorithm using binary data to suggest clusters of mutations that can easily be discriminated by cluster viewing schemes.

In the donation process, blood donors are screened for the level of hemoglobin or hematocrit in order to protect them from developing anemia. Nevertheless, there is no standard procedure to predict anemia development after blood donation. The São Paulo Blood Center is responsible for maintaining a database with information on each donation. However, this database doesn't have a good quality and consequently it is difficult to establish systematic analysis using the donation database without previous data validations. In order to provide a better quality of donation data, this paper presents a provenance description based on a classification criteria defined by specialists. More concretely, this paper answers the follow main question: is there a connection between blood donation and decrease in hematocrit level in order to prevent undesirable outcomes to blood donors? In this paper we show that it is possible to provide detailed investigations in order to answer this main question using the data description without the need to impose changes in the current database system structures that is sponsored by São Paulo Blood Center.

Precision Agriculture (PA) comprises a set of tools to understand and manage inherent spatial variability within crop fields. PA relies on a variety of techniques to collect, analyze, process, and synthesize voluminous geo referenced data. However, prior to large-scale practice, PA requires a successful experimentation stage, which is the present stage of PA for the sugarcane system. This paper presents a data analysis workflow for PA experiments, including workflow application to a case study in a sugarcane area where an appreciable diversity of soil and plant attributes has been measured. Our data analysis workflow has basis on: i) removal of outliers, ii) representation of different data acquisition techniques on a common spatial grid, iii) estimation of typical "noise" level in each measured attribute, iv) spatial autocorrelation analysis for each attribute, v) correlation analysis to identify related attributes, and vi) principal component analysis to reduce the dimensionality of the attribute space. By treating the diversity of measured attributes on a common ground, the proposed analysis workflow guides further experimentation as well as selection of data acquisition technologies suitable for large-scale sugarcane PA.

Software engineering has greatly evolved in recent years. Today applications are deployed on heterogeneous distributed infra-structure from mobile devices to cloud computing. Service-oriented architectures, such as SOA and REST Web Services, have been widely used to efficiently design high-availability, scalable and reliable systems for dynamic business environments based on a distributed infra-structure. Despite the improvements these architectures have made to enhance the evolvability of systems, there are some challenges that still need to be overcome. More concretely, service-based systems and development teams are constantly under pressure from business stakeholders who continuously increase their demands for changes in systems. This paper describes a configuration-based approach that can empower adaptive mechanisms in order to overcome this challenge. It presents a solution based on a centralized application configuration repository service specially designed as a RESTful web service API to provide the benefits of configuration, such as adaptability, to high-availability, scalable and loosely coupled systems, allowing them to respond quickly to changes. The solution was successfully implemented in an evolutionary online advertising system used by the largest Brazilian web-portal, responsible for processing 5 billion ad requests per month. It allowed the design of a self-adaptive advertisement ranking mechanism that continuously evolves the system configuration, without human supervision. The adoption of this solution was responsible for a drastic increase in the amount of changes applied in this advertising environment. It also greatly reduced the time from conceiving a new change to having it working in the system. Moreover, the solution is available as open source and it has also being used by several other service-based systems.

Mutations in HIV patients' reverse transcriptase and protease may be related to drug resistance. There are many issues that make difficult the complete elucidation of the relationship between these mutations and drug resistance, such as cross resistance and the limitations to detect the relevance of resistance. Look up tables and rule-based systems are an attempt to classify sequences and predict treatment failure. However, they depend on the scientific literature and their quality and reliability. Data-intensive analysis of HIV mutation databases may help to corroborate or to improve such knowledge spread in the literature. Pattern recognition algorithms classify data extracting information from different data domain. Clustering and biclustering classification algorithms have been explored to group scientific and business data based on measures of similarities. K-means is a popular algorithm for clustering and Bimax is used with binary data. Considering this scenario, the main contribution of this work is to develop a new methodology based on K-means and Bimax using a binary data representation of reverse transcriptase and protease sequences, in an attempt to get an unsupervised classification of the sequences that may be related to drug resistance. In our work, 14,393 sequences with selected positions of the proteins, known to be related to drug resistance, represented in an 82-dimensional vector space are analyzed by pattern recognition algorithms. The sequences are represented as binary vectors. Suitable visualization of such vectors is produced for medical interpretation and indicates some correspondence to the prediction of drug resistance given by the brazilian look up table, used by brazilian physicians, but that depends on the literature on HIV and it's quality to be created. As a consequence, in this work we describe a methodology based on the application of pattern recognition algorithms using binary data in order to suggest clusters of mutations and their relations with drug resistance using a different cluster visualization scheme.

Building mission critical applications and services (e.g. e-commerce) using process-oriented approaches has had successes and difficulties. These applications automated successfully the important frequent cases such as purchases, but the code needed for handling exceptions such as cancellations and failures tend to grow to disproportionate size and complexity. These difficulties lead to non-automated and expensive solutions such as call centers, which resolve data inconsistency problems manually. In this paper we describe the WED-flow (work, event, and data-flow) approach, which provides transactional recovery through incremental evolution of exception handling, by combining the concepts of advanced transaction models, events, and data states. By carefully recording the detailed data states of each execution step, WED-flow composes backward and forward recovery mechanisms as reusable exception handling services to preserve the consistency of all databases involved in the application with well-defined correctness properties. A practical application of the automated recovery in WED-flow is the real-time recovery of failed cases for mission-critical applications and services.

Collaborative applications require understanding of the theoretical foundations. In case of workflow systems, one possibility to achieve this is an accurate description of workflow functionalities. Despite its growing popularity and success, it has not yet been evaluated whether Language of Temporal

Co-authored documents are becoming increasingly important for knowledge representation and sharing. Tools for supporting document co-authoring are expected to satisfy two requirements: 1) querying changes over editing histories; 2) maintaining data consistency among users. Current tools support either limited queries or are not suitable for loosely controlled collaborative editing scenarios. We address both problems by proposing a new persistent data structure-partial persistent sequence. The new data structure enables us to create unique character identifiers that can be used for associating meta-information and tracking their changes, and also design simple view synchronization algorithms to guarantee data consistency under the presence of concurrent updates. Experiments based on real-world collaborative editing traces show that our data structure uses disk space economically and provides efficient performance for document update and retrieval.

Despite recent advances in many real-time and workflow management systems (WFMS), event-handling is still a manual or semi-automated task. The integration of automated event processing with workflows remains an open research challenge to both academic and industrial communities. In this work, we propose a concrete approach that logs interactions between workflow component activities in the form of data states that accurately and efficiently store necessary information for event-handling. Our approach (called WED-flow) explicitly represents various dependencies and constraints of a WFMS in sophisticated data states. Due to the availability of this large amount of historic information, our approach is able to support a flexible event-handling in WFMS. In this paper we present definitions for workflow management systems that incorporate events, and characterize such systems using the WED-flow approach. We also present a scientific workflow example in genetic testing to illustrate the advantages of integrating events with workflow through the WED-flow approach.

Business process integration is a serious challenge in collaborative information systems due to the potential interference among them. This paper describes RiverFish architecture to solving integration problems in collaborative information systems that belong to e-commerce environment. DECA application has been used to show a good example of a non-trivial problem of this integration. DECA application controls the processing application involving several government agencies to illustrate a new application called DECA. Each step of DECA processes various levels of check points and stores the results into associated collaborative information systems. This application has served more than 2 million users since 2000, demonstrating the reliability and support for evolution of RiverFish approach

Data warehouse systems (DWS) make use of storage techniques for efficient end user accessing and query facilities. DWS applications have implemented classic data synchronism operations that do not support an immediate data update. With the evolution of semantic data representation in the operational database environment, the accomplished analysis in DWS demands new synchronism ways. Hence, there is a growing interest in DWS that can rapidly absorb the operational database updates, without compromising the operational query processes. Our research aims at the characterization of the synchronous and asynchronous algorithms limits for data updating in a DWS. This research proposes another way for update propagations of asynchronous transactions in DWS, the dw-log algorithm. The dw-log algorithm implementation is supported by the process algebra approach.

A data warehouse can be viewed as a set of portions of information from transactional systems (OLTP - on-line transaction processing) with different business characteristics and used by analytical applications (OLAP - on-line analytical processing) with different user requirements. As the concept of real time enterprise evolves, the synchronism between transaction data and data warehouse, statically implemented, has been reviewed. This paper proposes a hybrid synchronism model for the data warehouse. In this model, portions of information are analyzed by a set of parameters and searching functions. With the parameters and searching functions in mind we can go on to choosing the most suitable synchronism option for each portion of information.

Medium-sized and large private companies and public institutions usually have several operational units that enjoy increasing scope to define their own rules for the development of their activities, procedures and modus operandi. From the point of view of the development of specific software to support the activities of these organizations, this freedom means that systems must be sufficiently flexible not only to allow them to be configured to fulfil each unit's needs and specific modes of operation but also to meet the needs of interaction among them through information exchange. In the case of an application consisting of several subsystems, the existence of a data repository common to all these subsystems means that, although parts of the database are accessed by only one subsystem, the data are stored in a monolithic form, translating into a loss of autonomy of the subsystems involved. The objective of this work is to propose guidelines for the modularization of databases, for both corporate data center schemas and specific schemas for independent units. This modularization not only supports the solutions to the interoperability but also enables information systems based on the data models to be really flexible.