A Distributed, Architecture-Centric Approach to Computing Accurate Recommendations from Very Large and Sparse Datasets

The use of recommender systems is an emerging trend today, when user behavior information is abundant. There are many large datasets available for analysis because many businesses are interested in future user opinions. Sophisticated algorithms that predict such opinions can simplify decision-making, improve customer satisfaction, and increase sales. However, modern datasets contain millions of records, which represent only a small fraction of all possible data. Furthermore, much of the information in such sparse datasets may be considered irrelevant for making individual recommendations. As a result, there is a demand for a way to make personalized suggestions from large amounts of noisy data. Current recommender systems are usually all-in-one applications that provide one type of recommendation. Their inflexible architectures prevent detailed examination of recommendation accuracy and its causes. We introduce a novel architecture model that supports scalable, distributed suggestions from multiple independent nodes. Our model consists of two components, the input matrix generation algorithm and multiple platform-independent combination algorithms. A dedicated input generation component provides the necessary data for combination algorithms, reduces their size, and eliminates redundant data processing. Likewise, simple combination algorithms can produce recommendations from the same input, so we can more easily distinguish between the benefits of a particular combination algorithm and the quality of the data it receives. Such flexible architecture is more conducive for a comprehensive examination of our system. We believe that a user's future opinion may be inferred from a small amount of data, provided that this data is most relevant. We propose a novel algorithm that generates a more optimal recommender input. Unlike existing approaches, our method sorts the relevant data twice. Doing this is slower, but the quality of the resulting input is considerably better. Furthermore, the modular nature of our approach may improve its performance, especially in the cloud computing context. We implement and validate our proposed model via mathematical modeling, by appealing to statistical theories, and through extensive experiments, data analysis, and empirical studies. Our empirical study examines the effectiveness of accuracy improvement techniques for collaborative filtering recommender systems. We evaluate our proposed architecture model on the Netflix dataset, a popular (over 130,000 solutions), large (over 100,000,000 records), and extremely sparse (1.1\%) collection of movie ratings. The results show that combination algorithm tuning has little effect on recommendation accuracy. However, all algorithms produce better results when supplied with a more relevant input. Our input generation algorithm is the reason for a considerable accuracy improvement

Prime Number-Based Hierarchical Data Labeling Scheme for Relational Databases

Hierarchical data structures are an important aspect of many computer science fields including data mining, terrain modeling, and image analysis. A good representation of such data accurately captures the parent-child and ancestor-descendent relationships between nodes. There exist a number of different ways to capture and manage hierarchical data while preserving such relationships. For instance, one may use a custom system designed for a specific kind of hierarchy. Object oriented databases may also be used to model hierarchical data. Relational database systems, on the other hand, add an additional benefit of mature mathematical theory, reliable implementations, superior functionality and scalability. Relational databases were not originally designed with hierarchical data management in mind. As a result, abstract information can not be natively stored in database relations. Database labeling schemes resolve this issue by labeling all nodes in a way that reveals their relationships. Labels usually encode the node's position in a hierarchy as a number or a string that can be stored, indexed, searched, and retrieved from a database. Many different labeling schemes have been developed in the past. All of them may be classified into three broad categories: recursive expansion, materialized path, and nested sets. Each model has its strengths and weaknesses. Each model implementation attempts to reduce the number of weaknesses inherent to the respective model. One of the most prominent implementations of the materialized path model uses the unique characteristics of prime numbers for its labeling purposes. However, the performance and space utilization of this prime number labeling scheme could be significantly improved. This research introduces a new scheme called reusable prime number labeling (rPNL) that reduces the effects of the mentioned weaknesses. The proposed scheme advantage is discussed in detail, proven mathematically, and experimentally confirmed

mFrame: An Application Framework for Mobile Resource-constrained Computing Environments

The component and architectural design reuse value of application frameworks enables higher productivity, faster turn-around time, and reduced cost when compared with the traditional development approaches. Because of the constrained nature of mobile devices, the application frameworks for such a computing environment have to provide a simple and lightweight implementation while still maintaining their flexibility and reusability value. This paper presents mobile Framework (mFrame) application framework that can be used for deploying software applications to compact mobile devices such as cellular phones, personal digital assistants (PDA), global positioning systems (GPS) etc. It introduces a queuing and service layer architecture that provides simple data exchange mechanism between the presentation components and local and remote business services. Compact mobile devices using this architecture will be able to handle network disconnections, because the requests will be saved; and information will be exchanged with the remote services upon reconnection

Reusable Prime Number Labeling Scheme for Hierarchical Data Representation in Relational Databases

Hierarchical data structures are important for many computing and information science disciplines including data mining, terrain modeling, and image analysis. There are many specialized hierarchical data management systems, but they are not always available. Alternatively, relational databases are far more common and offer superior reliability, scalability, and performance. However, relational databases cannot natively store and manage hierarchical data. Labeling schemes resolve this issue by labeling all nodes with alphanumeric strings that can be safely stored and retrieved from a database. One such scheme uses prime numbers for its labeling purposes, however the performance and space utilization of this method are not optimal. We propose a more efficient and compact version of this approach

Квантова інформаційна технологія на краю

This is an introductory text to a collection of selected papers from the Joint Workshop on the Quantum Information Technologies and Edge Computing (QuaInT & doors 2021) which were held in Zhytomyr, Ukraine, on the April 11, 2021. It consists of short summaries of selected papers and some observations about the events.Це вступний текст до збірки вибраних доповідей зі Спільного семінару з квантових інформаційних технологій та граничних обчислень (QuaInT & doors 2021), який відбувся у Житомирі, Україна, 11 квітня 2021 р. Він складається з коротких резюме вибраних статей та деяких спостережень щодо подій

Prime number-based hierarchical data labeling scheme for relational databases

Thesis (M.S.)--University of Kansas, Electrical Engineering & Computer Science, 2007.Hierarchical data structures are an important aspect of many computer science fields including data mining, terrain modeling, and image analysis. A good representation of such data accurately captures the parent-child and ancestor-descendent relationships between nodes. There exist a number of different ways to capture and manage hierarchical data while preserving such relationships. For instance, one may use a custom system designed for a specific kind of hierarchy. Object oriented databases may also be used to model hierarchical data. Relational database systems, on the other hand, add an additional benefit of mature mathematical theory, reliable implementations, superior functionality and scalability. Relational databases were not originally designed with hierarchical data management in mind. As a result, abstract information can not be natively stored in database relations. Database labeling schemes resolve this issue by labeling all nodes in a way that reveals their relationships. Labels usually encode the node's position in a hierarchy as a number or a string that can be stored, indexed, searched, and retrieved from a database. Many different labeling schemes have been developed in the past. All of them may be classified into three broad categories: recursive expansion, materialized path, and nested sets. Each model has its strengths and weaknesses. Each model implementation attempts to reduce the number of weaknesses inherent to the respective model. One of the most prominent implementations of the materialized path model uses the unique characteristics of prime numbers for its labeling purposes. However, the performance and space utilization of this prime number labeling scheme could be significantly improved. This research introduces a new scheme called reusable prime number labeling (rPNL) that reduces the effects of the mentioned weaknesses. The proposed scheme advantage is discussed in detail, proven mathematically, and experimentally confirmed

Pseudorapidity densities of charged particles with transverse momentum thresholds in pp collisions at √ s = 5.02 and 13 TeV

The pseudorapidity density of charged particles with minimum transverse momentum (pT) thresholds of 0.15, 0.5, 1, and 2 GeV/c is measured in pp collisions at the center of mass energies of √s=5.02 and 13 TeV with the ALICE detector. The study is carried out for inelastic collisions with at least one primary charged particle having a pseudorapidity (η) within 0.8pT larger than the corresponding threshold. In addition, measurements without pT-thresholds are performed for inelastic and nonsingle-diffractive events as well as for inelastic events with at least one charged particle having |η|2GeV/c), highlighting the importance of such measurements for tuning event generators. The new measurements agree within uncertainties with results from the ATLAS and CMS experiments obtained at √s=13TeV.

Multiplicity dependence of K*(892)0 and ϕ(1020) production in pp collisions at t √s=13 TeV

The striking similarities that have been observed between high-multiplicity proton-proton (pp) collisions and heavy-ion collisions can be explored through multiplicity-differential measurements of identified hadrons in pp collisions. With these measurements, it is possible to study mechanisms such as collective flow that determine the shapes of hadron transverse momentum (pT) spectra, to search for possible modifications of the yields of short-lived hadronic resonances due to scattering effects in an extended hadron-gas phase, and to investigate different explanations provided by phenomenological models for enhancement of strangeness production with increasing multiplicity. In this paper, these topics are addressed through measurements of the K∗(892)0 and φ(1020) mesons at midrapidity in pp collisions at √s = 13 TeV as a function of the charged-particle multiplicity. The results include the pT spectra, pT-integrated yields, mean transverse momenta, and the ratios of the yields of these resonances to those of longer-lived hadrons. Comparisons with results from other collision systems and energies, as well as predictions from phenomenological models, are also discussed

Multiplicity dependence of inclusive J/ψ production at midrapidity in pp collisions at √s=13 TeV

Measurements of the inclusive J/ψ yield as a function of charged-particle pseudorapidity density dNch/dη in pp collisions at √s = 13 TeV with ALICE at the LHC are reported. The J/ψ meson yield is measured at midrapidity (|y| < 0.9) in the dielectron channel, for events selected based on the charged-particle multiplicity at midrapidity (|η| < 1) and at forward rapidity (−3.7 < η < −1.7 and 2.8 < η < 5.1); both observables are normalized to their corresponding averages in minimum bias events. The increase of the normalized J/ψ yield with normalized dNch/dη is significantly stronger than linear and dependent on the transverse momentum. The data are compared to theoretical predictions, which describe the observed trends well, albeit not always quantitatively