Modern object stores expose only coarse, static configurations (replication factor, ACLs, bucket lifecycles) and must be over-provisioned to absorb demand spikes or new security requirements. MictlanX upgrades the storage layer itself with two code-defined programming models: a Responsive-Deployment Model, that lets operators declare elastic regions in YAML/Python, and an Adaptive Data-Placement Model, that drives per-object filters and dynamic replication. A 16-node prototype sustains 20 MB/s under a 0.01s burst while keeping 90% of requests below 0.1s---up to 4× faster than fixed-replica modes---and beats MinIO, Google Drive, and Dropbox by up to 23% throughput with sub-second latencies.

Content delivery and sharing (CDS) is a popular and cost effective cloud-based service for organizations to deliver/share contents to/with end-users, partners and insider users. This type of service improves the data availability and I/O performance by producing and distributing replicas of shared contents. However, such a technique increases overhead on the storage/network resources. This article introduces a threefold methodology to improve the trade-off between I/O performance and capacity utilization of cloud storage for CDS services. This methodology includes: i) Definition of a classification model for identifying types of users and contents by analyzing their consumption/ demand and sharing patterns, ii) Usage of the classification model for defining content availability and load balancing schemes, and iii) Integration of a dynamic availability scheme into a cloud-based CDS system. Our method was implemented on both a simulator and a real-world CDS service, supporting information sharing operations performed in a cloud storage. An experimental evaluation, conducted in a private cloud through simulation and emulation of workloads, showed the feasibility of this methodology in terms of storage capacity utilization, whereas the real-world implementation revealed the efficiency of applying a classification model to information sharing patterns in terms of I/O performance.

A common task in data analysis is to find the appropriate data sample whose properties allow us to infer the parameters and behavior of the data population. In data mining this task makes sense since usually the population is significantly huge, and thus it is required (for practical reasons) to ob tain a subset that preserves its properties. In this regard, statistics offers some sampling techniques usually based on asymptotic results from the Central Limit Theorem. The effectiveness of such ways is bounded by several considerations as the sampling strategy (simple with or without replacement, stratified, cluster-based, etc.), the size of the population and the dimensionality of the space of the data. Due to these considerations alternative proposals are necessary. We propose a method based on a measure of information in terms of Shannon’s Entropy. Our idea is to find the optimal sample whose information is as similar as possible to the information of the population, subject to several constraints. Finding such sample represents a hard optimization problem whose feasible space disallows the use of traditional optimization techniques. To solve it, we resort to a breed of Genetic Algorithm called Eclectic Genetic Algorithm. We test our method with synthetic datasets; the results show that our method is suitable. For completeness, we used several datasets from real problems; the results confirm the effectiveness of our proposal and allow us to visualize different applications. Finally, we establish a baseline based on selection instance methods as a point reference to measure the effectiveness of our method.

Integrated health services are characterized by a high degree of collaboration and communication among health professionals, as well as a merge of political, administrative, and technical actions, which can allow the sharing of information among healthcare team members (physicians, nurses, managers, and other stakeholders) related to patient care, and access to hospital infrastructure and technology, within a patient-centered approach. In this paper, we propose a technological solution based on software agents, which allows supporting the management of collaborative processes in environments of dynamic collaborative networks. We present a methodology for coordinating healthcare services through collaborative processes to enable organizations providing integrated care services and continuous process improvements. This methodology includes methods based on Model-Driven Development, which enable the generation of executable process models and the code of the process-oriented agents, derived from conceptual models of collaborative processes. The methodology and methods are implemented and automated by software agents that enable the generation of the technological solution, at run-time on the platform. Furthermore, we propose an agent-based platform that enables organizations to negotiate collaboration agreements in electronic format to establish collaborative networks, as well as a definition of the collaborative processes to be executed. In this way, the proposed agent-based platform allows collaborations among heterogeneous and autonomous healthcare organizations focusing on the process-oriented integration, enabling us to provide integrated healthcare services.

Abstract Relational databases (RDBs) have been widely used as back end for information systems. Considering that RDBs have valuable knowledge interwoven in between stored data, how to access, represent and share this knowledge becomes an important challenge. Topic maps (TMs) emerge as a good solution for this problem. However, manual development of TMs is a difficult, time‐consuming and subjective task if there is no common guideline. The existing TMs building approaches mainly consider the meta‐information contained in a RDB, without considering the knowledge residing in the database content (its current state). Other approaches require a predefined configuration for applying a specific data transformation. This paper proposes an automatic method for TM construction based on learning rules. Our method considers the background knowledge of the RDBs during the building process and was implemented and applied on a representative set of 15 RDBs. The resulting TMs were validated syntactically using a standard tool and validated semantically through the inference of information using a formal query language. In addition, an analysis between the relational data (input) and its representation (output) was conducted. The results found in our experiments are encouraging and put in evidence the soundness of the proposed method.

Nowadays, users of mobile phones generate too many files that have to be frequently downloaded to an external storage repository, restricting the user mobility. This paper presents a File Transfer Service (FTS) for mobile phones with scare storage resources. It is a support that can be used through a set of functions (API) that facilitates file transfer between mobile applications and external storage servers, taking advantage of different wireless networks. The FTS selects the best wireless connection (WiFi, GPRS or UMTS) considering accessibility and cost of the service. FTS is able to use the Multimedia Messaging Service (MMS) as an alternative option for transferring files, which is especially useful when the mobile phone connectivity is limited. It is based on the J2ME platform. As a use case, a mobile application named Swapper was built on top of the FTS. When the mobile phone memory runs out, Swapper automatically sends selected files to a web storage server using the best connection available, increasing the storage space in the mobile phone. Swapper includes an efficient replacement policy that minimizes the latency perceived by users.

This paper introduces an external storage middleware, that offers a set of functions (API - Application Program Interface) to mobile applications and facilitates the transfer of files between a mobile device and external storage servers regardless of the available wireless network. The Middleware selects the best wireless service available. In addition, the files exchanged between the mobile device and the external storage server are encrypted to provide security when traveling through the network. As a use case, it was developed an automatic file swapper service for mobile devices. The service is running on the mobile device optimizing its available storage space. Our middleware was tested with the following wireless services: Wi-Fi, GPRS, MMS.