In this chapter, we will restrict our attention to the ferrites and a few other closely related materials. The great interest in ferrites stems from their unique combination of a spontaneous magnetization and a high electrical resistivity. The observed magnetization results from the difference in the magnetizations of two non-equivalent sub-lattices of the magnetic ions in the crystal structure. Materials of this type should strictly be designated as “ferrimagnetic” and in some respects are more closely related to anti-ferromagnetic substances than they are to ferromagnetics in which the magnetization results from the parallel alignment of all the magnetic moments present. We shall not adhere to this special nomenclature except to emphasize effects, which are due to the existence of the sub-lattices.

Ferromagnetic materials

Many companies are increasingly using MapReduce for efficient large scale data processing such as personalized advertising, spam detection, and different data mining tasks. Cloud computing offers an attractive option for businesses to rent a suitable size Hadoop cluster, consume resources as a service, and pay only for resources that were utilized. One of the open questions in such environments is the amount of resources that a user should lease from the service provider. Often, a user targets specific performance goals and the application needs to complete data processing by a certain time deadline. However, currently, the task of estimating required resources to meet application performance goals is solely the users' responsibility. In this work, we introduce a novel framework and technique to address this problem and to offer a new resource sizing and provisioning service in MapReduce environments. For a MapReduce job that needs to be completed within a certain time, the job profile is built from the job past executions or by executing the application on a smaller data set using an automated profiling tool. Then, by applying scaling rules combined with a fast and efficient capacity planning model, we generate a set of resource provisioning options. Moreover, we design a model for estimating the impact of node failures on a job completion time to evaluate worst case scenarios. We validate the accuracy of our models using a set of realistic applications. The predicted completion times of generated resource provisioning options are within 10% of the measured times in our 66-node Hadoop cluster.

Resource provisioning framework for mapreduce jobs with performance goals

This study reports the various physical properties of (1�x)(0.3CoFe2O4-0.7BiFeO3)-xBaTiO3 composites (equivalently denoted as 0.3CFO-0.7BFO/BT) with the compositions x=0, 0.30, 0.35, 0.40 and 1.0. The composites are synthesized through a hybrid processing technique in which 0.3CFO–0.7BFO is prepared through a sol-gel process, and BT is processed through a solid state reaction method. Subsequently, the effects of the addition of BT on the structural, dielectric, magnetic and magneto-dielectric properties of 0.3CFO–0.7BFO have been investigated for various BT concentrations. The Rietveld refinement analysis of x-ray diffraction patterns reveals the structural distortion in the BFO phase with the addition of BT, while no such distortion has been observed for the CFO phase. Energy dispersive spectroscopy confirms the presence of two types of grains that correspond to the 0.3CFO–0.7BFO and BT phases in field emission scanning electron micrographs of the composites. Improved dielectric properties have been observed, which are associated with the improved density of composites with the addition of BT. Measurements of the magnetic and ferroelectric hysteresis loops at room temperature indicate that the composites exhibit ferroelectricity and ferromagnetism simultaneously at room temperature. An increase of the electric polarization has been observed due to structural distortion arising with the addition of BT. The significant dependence of the dielectric constant on the magnetic field has been observed in the prepared composites. The highest value of the magneto-dielectric response (3.2%) has been observed for a 40 mol% addition of BT.

https://iopscience.iop.org/article/10.1088/0964-1726/23/10/105024/pdf

Effect of BaTiO 3 addition on structural, multiferroic and magneto-dielectric properties of 0.3CoFe 2 O 4 -0.7BiFeO 3 ceramics

Multiferroic and magnetoelectric properties of Ba0.85Ca0.15Zr0.1Ti0.9O3–CoFe2O4 core–shell nanocomposite

The variety of data is one of the most challenging issues for the research and practice in data management systems. The data are naturally organized in different formats and models, including structured data, semi-structured data, and unstructured data. In this survey, we introduce the area of multi-model DBMSs that build a single database platform to manage multi-model data. Even though multi-model databases are a newly emerging area, in recent years, we have witnessed many database systems to embrace this category. We provide a general classification and multi-dimensional comparisons for the most popular multi-model databases. This comprehensive introduction on existing approaches and open problems, from the technique and application perspective, make this survey useful for motivating new multi-model database approaches, as well as serving as a technical reference for developing multi-model database applications.

/pdf/multi-model-databases-a-new-journey-to-handle-the-variety-of-2xrnvei8mn.pdf

Multi-model Databases: A New Journey to Handle the Variety of Data

Multiferroic composites that comprise NiFe2O4(NFO) and Na0.5Bi0.5TiO3(NBT) are synthesized using the sol-gel method. The results of XRD and scanning electron microscopy indicate the single-phase formation of NFO and NBT and the presence of two phases in the composites. The dielectric constant and dielectric loss are determined as functions of frequency over a wide range of frequencies from 40 Hz to 1 MHz. Room-temperature magnetization measurements show that these composites are soft magnetic. Further, the multiferroic character is confirmed by their magnetoelectric (ME) response at room temperature. The optimal ME response is 0.14% in a 67NFO–33NBT composite. The present study demonstrates that NFO–NBT could be effective as a lead-free multiferroic composite and provides an alternative for environmental-friendly ME devices.

Magnetoelectric response in lead-free multiferroic NiFe2O4–Na0.5Bi0.5TiO3 composites

Multiferroic composites of NiFe2O4 (NFO) and BiFe0.5Cr0.5O3 (BFCO) are synthesized and their dielectric, magnetic, and magnetoelectric (ME) properties are measured at room temperature. NFO-BFCO forms a two-phase composite. The composite has a larger magnetization and dielectric constant than those of both parent compounds, due to the effects of interfacial strain on BFCO. Furthermore, the ME response in NFO-BFCO is about one time larger than that of BFCO, revealing the success of magnetic control of the dielectric response via the mechanical coupling, which can be exploited in the future applications of multiferroic composites.

Dielectric, magnetic and magnetoelectric properties of multiferroic BiFe0.5Cr0.5O3–NiFe2O4 composites

Big data processing systems (e.g., Hadoop, Spark, Storm) contain a vast number of configuration parameters controlling parallelism, I/O behavior, memory settings, and compression. Improper parameter settings can cause significant performance degradation and stability issues. However, regular users and even expert administrators grapple with understanding and tuning them to achieve good performance. We investigate existing approaches on parameter tuning for both batch and stream data processing systems and classify them into six categories: rule-based, cost modeling, simulation-based, experiment-driven, machine learning, and adaptive tuning. We summarize the pros and cons of each approach and raise some open research problems for automatic parameter tuning.

https://dl.acm.org/doi/pdf/10.1145/3381027

A Survey on Automatic Parameter Tuning for Big Data Processing Systems

Database and big data analytics systems such as Hadoop and Spark have a large number of configuration parameters that control memory distribution, I/O optimization, parallelism, and compression. Improper parameter settings can cause significant performance degradation and stability issues. However, regular users and even expert administrators struggle to understand and tune them to achieve good performance. In this tutorial, we review existing approaches on automatic parameter tuning for databases, Hadoop, and Spark, which we classify into six categories: rule-based, cost modeling, simulation-based, experiment-driven, machine learning, and adaptive tuning. We describe the foundations of different automatic parameter tuning algorithms and present pros and cons of each approach. We also highlight real-world applications and systems, and identify research challenges for handling cloud services, resource heterogeneity, and real-time analytics.

/pdf/speedup-your-analytics-automatic-parameter-tuning-for-qkla9kq7oa.pdf

Speedup your analytics: automatic parameter tuning for databases and big data systems

Unlike traditional database management systems which are organized around a single data model, a multi-model database (MMDB) utilizes a single, integrated back-end to support multiple data models, such as document, graph, relational, and key-value. As more and more platforms are proposed to deal with multi-model data, it becomes crucial to establish a benchmark for evaluating the performance and usability of MMDBs. Previous benchmarks, however, are inadequate for such scenario because they lack a comprehensive consideration for multiple models of data. In this paper, we present a benchmark, called UniBench, with the goal of facilitating a holistic and rigorous evaluation of MMDBs. UniBench consists of a mixed data model, a synthetic multi-model data generator, and a set of core workloads. Specifically, the data model simulates an emerging application: Social Commerce, a Web-based application combining E-commerce and social media. The data generator provides diverse data format including JSON, XML, key-value, tabular, and graph. The workloads are comprised of a set of multi-model queries and transactions, aiming to cover essential aspects of multi-model data management. We implemented all workloads on ArangoDB and OrientDB to illustrate the feasibility of our proposed benchmarking system and show the learned lessons through the evaluation of these two multi-model databases. The source code and data of this benchmark can be downloaded at http://udbms.cs.helsinki.fi/bench/.

/pdf/unibench-a-benchmark-for-multi-model-database-management-zczmr5fnl9.pdf

Yuxing Chen

Papers

Magnetoelectric response in lead-free multiferroic NiFe2O4–Na0.5Bi0.5TiO3 composites

Dielectric, magnetic and magnetoelectric properties of multiferroic BiFe0.5Cr0.5O3–NiFe2O4 composites

A Survey on Automatic Parameter Tuning for Big Data Processing Systems

Speedup your analytics: automatic parameter tuning for databases and big data systems

UniBench: A Benchmark for Multi-model Database Management Systems