Ideal convergent subseries in Banach spaces

Assume that $\mathcal{I}$ is an ideal on $\mathbb{N}$, and $\sum_n x_n$ is a divergent series in a Banach space $X$. We study the Baire category, and the measure of the set $A(\mathcal{I}):=\left\{t \in \{0,1\}^{\mathbb{N}} \colon \sum_n t(n)x_n \textrm{ is } \mathcal{I}\textrm{-convergent}\right\}$. In the category case, we assume that $\mathcal{I}$ has the Baire property and $\sum_n x_n$ is not unconditionally convergent, and we deduce that $A(\mathcal{I})$ is meager. We also study the smallness of $A(\mathcal{I})$ in the measure case when the Haar probability measure $\lambda$ on $\{0,1\}^{\mathbb{N}}$ is considered. If $\mathcal{I}$ is analytic or coanalytic, and $\sum_n x_n$ is $\mathcal{I}$-divergent, then $\lambda(A(\mathcal{I}))=0$ which extends the theorem of Dindo\v{s}, \v{S}al\'at and Toma. Generalizing one of their examples, we show that, for every ideal $\mathcal{I}$ on $\mathbb{N}$, with the property of long intervals, there is a divergent series of reals such that $\lambda(A(Fin))=0$ and $\lambda(A(\mathcal{I}))=1$

Understanding spatial data usability

In recent geographical information science literature, a number of researchers have made passing reference to an apparently new characteristic of spatial data known as 'usability'. While this attribute is well-known to professionals engaged in software engineering and computer interface design and testing, extension of the concept to embrace information would seem to be a new development. Furthermore, while notions such as the use and value of spatial information, and the diffusion of spatial information systems, have been the subject of research since the late-1980s, the current references to usability clearly represent something which extends well beyond that initial research. Accordingly, the purposes of this paper are: (1) to understand what is meant by spatial data usability; (2) to identify the elements that might comprise usability; and (3) to consider what the related research questions might be

Chmura w bibliotece jako lekarstwo na chmury nad biblioteką na przykładzie bibliotek pedagogicznych

Using the cloud computing has become univer sal nowadays. It permeated into private and professional life. The article presents examples of the ways of using cloud computing on various types of devices by teacher-librarians. The author assumes that cloud computing can solve problems related to the implementation of statutory tasks of libraries in some situations. Services available in the cloud are investigated by informatologists and topics trainings are often organized, both by librarians and for librarians. According to the author, working in the cloud does not pose a threat to the identity of the pedagogical library, but it is only a new tool to support activities of such an institution. Farthermore, cloud computing should be assumed with the knowledge of its capabilities and limitations

Combining edge and cloud computing for mobility analytics

Mobility analytics using data generated from the Internet of Mobile Things (IoMT) is facing many challenges which range from the ingestion of data streams coming from a vast number of fog nodes and IoMT devices to avoiding overflowing the cloud with useless massive data streams that can trigger bottlenecks [1]. Managing data flow is becoming an important part of the IoMT because it will dictate in which platform analytical tasks should run in the future. Data flows are usually a sequence of out-of-order tuples with a high data input rate, and mobility analytics requires a real-time flow of data in both directions, from the edge to the cloud, and vice-versa. Before pulling the data streams to the cloud, edge data stream processing is needed for detecting missing, broken, and duplicated tuples in addition to recognize tuples whose arrival time is out of order. Analytical tasks such as data filtering, data cleaning and low-level data contextualization can be executed at the edge of a network. In contrast, more complex analytical tasks such as graph processing can be deployed in the cloud, and the results of ad-hoc queries and streaming graph analytics can be pushed to the edge as needed by a user application. Graphs are efficient representations used in mobility analytics because they unify knowledge about connectivity, proximity and interaction among moving things. This poster describes the preliminary results from our experimental prototype developed for supporting transit systems, in which edge and cloud computing are combined to process transit data streams forwarded from fog nodes into a cloud. The motivation of this research is to understand how to perform meaningfulness mobility analytics on transit feeds by combining cloud and fog computing architectures in order to improve fleet management, mass transit and remote asset monitoringComment: Edge Computing, Cloud Computing, Mobility Analytics, Internet of Mobile Things, Edge Fog Fabri