Load Balancing for Entity Matching over Big Data using Sorted Neighborhood

Entity matching also known as entity resolution, duplicate identification, reference reconciliation or record linkage and is a critically important task for data cleaning and data integration. One can think of it, as the task of finding entities matching to the same entity in the real world. These entities can belong to a single source of data, or distributed data-sources. It takes structured data as an input and process includes comparison of that structured data (entity or database record) with entities present in the knowledge base. For large-scale entity, matching data has to go through some sequence of steps, which includes Evaluation, Preprocessing, Candidate calculation and Classification. The entity matching workflow consists of two strategies: blocking (map) and matching (reduce). Blocking strategy termed as the division of a data source into partitions or blocks. Blocking is helpful to improve performance. Blocking achieves this goal restricting the set of similar entities in the same partition or block and then, comparing the same within blocks. The partitioning makes use of blocking keys and blocking keys are determined from entity\u27s attributes. Partitioning helps to partition data into blocks. Values of one or several attributes form the blocking key. Mostly, the blocking key is concatenation of prefixes of these attributes. The second part of the workflow consists of the strategy for matching. This aims to identify all matching entity pairs within the same partition. To find out matching result, one need to realize comparison result of the pair of entities. A matching strategy can use several approaches for matching and can combine similarity scores to find if the entity pair is a match or not. The entity-matching model expects the matching strategy to return the list of matching pairs of entities. Thus, by relating the structured data with their most apposite entity, entity matching tries to gain the maximum out of the existing knowledge base. One of the best solutions for Entity Matching would be Dedoop [4], which is Deduplication of Hadoop. Cartesian product causes the workload due to execution with the time complexity of O (n2) and to provide more time for matching techniques to maintain the quality, some load balancing techniques are necessary. Even after the application of blocking, the task of matching i.e. Entity Matching can still be a costly task and can take up to several days for completion if running against large datasets. The MapReduce [2] programming model is perfect to execute EM in parallel. During execution, input file split into multiple parts or chunks. Then, map phase, multiple map tasks can read those parts in parallel, which are nothing but entities. During reduce phase, based on blocking keys, these entities are redistributed among several reduce tasks. This is helpful for grouping together entities with the same blocking key and can be helpful for the application of matching in parallel

Identification of low order models for large scale processes

Many industrial chemical processes are complex, multi-phase and large scale in nature. These processes are characterized by various nonlinear physiochemical effects and fluid flows. Such processes often show coexistence of fast and slow dynamics during their time evolutions. The increasing demand for a flexible operation of a complex process, a pressing need to improve the product quality, an increasing energy cost and tightening environmental regulations make it rewarding to automate a large scale manufacturing process. Mathematical tools used for process modeling, simulation and control are useful to meet these challenges. Towards this purpose, development of process models, either from the first principles (conservation laws) i.e. the rigorous models or the input-output data based models constitute an important step. Both types of models have their own advantages and pitfalls. Rigorous process models can approximate the process behavior reasonably well. The ability to extrapolate the rigorous process models and the physical interpretation of their states make them more attractive for the automation purpose over the input-output data based identified models. Therefore, the use of rigorous process models and rigorous model based predictive control (R-MPC) for the purpose of online control and optimization of a process is very promising. However, due to several limitations e.g. slow computation speed and the high modeling efforts, it becomes difficult to employ the rigorous models in practise. This thesis work aims to develop a methodology which will result in smaller, less complex and computationally efficient process models from the rigorous process models which can be used in real time for online control and dynamic optimization of the industrial processes. Such methodology is commonly referred to as a methodology of Model (order) Reduction. Model order reduction aims at removing the model redundancy from the rigorous process models. The model order reduction methods that are investigated in this thesis, are applied to two benchmark examples, an industrial glass manufacturing process and a tubular reactor. The complex, nonlinear, multi-phase fluid flow that is observed in a glass manufacturing process offers multiple challenges to any model reduction technique. Often, the rigorous first principle models of these benchmark examples are implemented in a discretized form of partial differential equations and their solutions are computed using the Computational Fluid Dynamics (CFD) numerical tools. Although these models are reliable representations of the underlying process, computation of their dynamic solutions require a significant computation efforts in the form of CPU power and simulation time. The glass manufacturing process involves a large furnace whose walls wear out due to the high process temperature and aggressive nature of the molten glass. It is shown here that the wearing of a glass furnace walls result in change of flow patterns of the molten glass inside the furnace. Therefore it is also desired from the reduced order model to approximate the process behavior under the influence of changes in the process parameters. In this thesis the problem of change in flow patterns as result of changes in the geometric parameter is treated as a bifurcation phenomenon. Such bifurcations exhibited by the full order model are detected using a novel framework of reduced order models and hybrid detection mechanisms. The reduced order models are obtained using the methods explained in the subsequent paragraphs. The model reduction techniques investigated in this thesis are based on the concept of Proper Orthogonal Decompositions (POD) of the process measurements or the simulation data. The POD method of model reduction involves spectral decomposition of system solutions and results into arranging the spatio-temporal data in an order of increasing importance. The spectral decomposition results into spatial and temporal patterns. Spatial patterns are often known as POD basis while the temporal patterns are known as the POD modal coefficients. Dominant spatio-temporal patterns are then chosen to construct the most relevant lower dimensional subspace. The subsequent step involves a Galerkin projection of the governing equations of a full order first principle model on the resulting lower dimensional subspace. This thesis can be viewed as a contribution towards developing the databased nonlinear model reduction technique for large scale processes. The major contribution of this thesis is presented in the form of two novel identification based approaches to model order reduction. The methods proposed here are based on the state information of a full order model and result into linear and nonlinear reduced order models. Similar to the POD method explained in the previous paragraph, the first step of the proposed identification based methods involve spectral decomposition. The second step is different and does not involve the Galerkin projection of the equation residuals. Instead, the second step involves identification of reduced order models to approximate the evolution of POD modal coefficients. Towards this purpose, two different methods are presented. The first method involves identification of locally valid linear models to represent the dynamic behavior of the modal coefficients. Global behavior is then represented by ‘blending’ the local models. The second method involves direct identification of the nonlinear models to represent dynamic evolution of the model coefficients. In the first proposed model reduction method, the POD modal coefficients, are treated as outputs of an unknown reduced order model that is to be identified. Using the tools from the field of system identification, a blackbox reduced order model is then identified as a linear map between the plant inputs and the modal coefficients. Using this method, multiple local reduced LTI models corresponding to various working points of the process are identified. The working points cover the nonlinear operation range of the process which describes the global process behavior. These reduced LTI models are then blended into a single Reduced Order-Linear Parameter Varying (ROLPV) model. The weighted blending is based on nonlinear splines whose coefficients are estimated using the state information of the full order model. Along with the process nonlinearity, the nonlinearity arising due to the wear of the furnace wall is also approximated using the RO-LPV modeling framework. The second model reduction method that is proposed in this thesis allows approximation of a full order nonlinear model by various (linear or nonlinear) model structures. It is observed in this thesis, that, for certain class of full order models, the POD modal coefficients can be viewed as the states of the reduced order model. This knowledge is further used to approximate the dynamic behavior of the POD modal coefficients. In particular, reduced order nonlinear models in the form of tensorial (multi-variable polynomial) systems are identified. In the view of these nonlinear tensorial models, the stability and dissipativity of these models is investigated. During the identification of the reduced order models, the physical interpretation of the states of the full order rigorous model is preserved. Due to the smaller dimension and the reduced complexity, the reduced order models are computationally very efficient. The smaller computation time allows them to be used for online control and optimization of the process plant. The possibility of inferring reduced order models from the state information of a full order model alone i.e. the possibility to infer the reduced order models in the absence of access to the governing equations of a full order model (as observed for many commercial software packages) make the methods presented here attractive. The resulting reduced order models need further system theoretic analysis in order to estimate the model quality with respect to their usage in an online controller setting

SYNTHESIS AND CHARACTERIZATION OF POLYMERIC ANTIOXIDANT DELIVERY SYSTEMS

Even though the role of oxidative stress in a variety of disease states is known, strategies to alleviate this oxidative stress by antioxidants have not been able to achieve clinical success. Particularly, treatment of oxidative stress by small molecule antioxidants has not received due attention because of the challenges associated with its delivery. Antioxidant polymers, where small molecule antioxidants are incorporated into the polymer backbone, are an emerging class of materials that can address some of these challenges. In this work, biodegradable polymers incorporating phenolic antioxidants in the polymer backbone were synthesized. Antioxidant polymers were then characterized for their in vitro degradation, antioxidant release and their effect on oxidative stress levels (redox state) in the cells. Trolox, a water-soluble analogue of vitamin E, was polymerized to synthesize poly(trolox ester) with 100% antioxidant content which undergoes biodegradation to release trolox. Nanoparticles of poly(trolox ester) were able to suppress oxidative stress injury induced by metal nanoparticles in an in vitro cell injury model. In another study, we polymerized polyphenolic antioxidants (e.g. curcumin, quercetin) using a modified non-free-radical polymerization poly(β-amino ester) chemistry. This synthesis scheme can be extended to all polyphenolic antioxidants and allows tuning of polymer degradation rate by choosing appropriate co-monomers from a large library of monomers available for β-amino ester chemistry. Poly(antioxidant β-amino esters) (PABAE) were synthesized and characterized for their degradation, cytotoxicity and antioxidant activity. PABAE degradation products suppressed oxidative stress levels in the cells confirming antioxidant activity of degradation products

Compounds and Methods for Reducing Oxidative Stress

Antioxidant polymeric compounds are provided that comprise a plurality of monomeric portions, where each monomeric portion includes an antioxidant molecule interposed between at least two acrylate molecules, and where at least one acrylate molecule of each monomeric portion is linked by a diamine molecule to an acrylate molecule of an adjacent monomeric portion to thereby form the polymer. Methods of synthesizing polymeric compounds and methods of using the antioxidant polymeric compounds to reduce oxidative stress are also provided

A rare presentation of atypical demyelination: tumefactive multiple sclerosis causing Gerstmann's syndrome

r. KS has been supported by a Higher Education Funding Council for England (HEFCE) Clinical Senior Lectureshi

A review on Phytosome loaded with novel herbal drug and their formulation, standardization and applications

Novel Drug Delivery System is need of time, as it makes bioavailability, securityand overall therapeutics of a drug easy-going and in the bat of an eye. In the recent days, most of the regnant maladies and nutritional disorders are treated with herbal medicines because of their less after math, economical and easily accessible. The potency of any herbal medication is contingent on the delivery of the effectual level of the therapeutically active constituent. But because of high polarity and poor lipophilicity, the active contents are incompletely assimilated resulting in poor bioavailability.Herbal drugs comprises of a vast array of active contents which furnishes us with a number of applications. But due to high polarity and poor lipophilicity the active contents are poorly absorbed resulting in poor bioavailability. These problems can be overcome by formulating a suitable novel preparation of the herbal extract. Phytosomes are one of the novel drug delivery system containing hydrophilic bioactive phytoconstituents of herbs surround and bound by phospholipids.This phytophospholipid complex resembles a little cell which exhibit better pharmacokinetic and pharmacodynamic profile than the conventional herbal extract resulting in better bioavailability. This article highlights recent information, commercial preparation of phytosomes as well as the various other novel approaches for delivery of herbal constituents. Keywords: Phytosomes, Bioavailability, Phosphatidylcholine, Phytoconstituents

Formulation and Evaluation of Traditional Antioxidant Grape Seeds Extract in the Form of Tablets

Oxygen uptake while breathing cause’s free radical production and in addition to that environmental factors such as pollutants, smoke and certain chemicals also contribute to their formation. Reactive oxygen species is a collective term that includes all reactive forms of oxygen, including both oxygen radicals and several non-radical oxidizing agents that participate in the initiation and/or propagation of chain reaction. Free radicals are atoms, molecules or ions with unpaired electrons that are highly unstable and active towards chemical reactions with other molecules. Antioxidant is any substance that when present at low concentrations compared to those of an oxidizable substrate significantly delays or prevents oxidation of that substrate. Antioxidants block the process of oxidation by neutralizing free radicals. Antioxidant power of proanthocyanidins is 20 times greater than vitamin E and 50 times greater than vitamin C. Proanthocyanidins in Grape seeds have been shown to exhibit strong antioxidant, antimutagenic, anti-inflammatory, anticarcinogenic and antiviral activity. Keywords- Antioxidants, Grape seed, Proanthocyanidins, DPPH activity

Biocompatibility of implantable materials: an oxidative stress viewpoint

Oxidative stress occurs when the production of oxidants surpasses the antioxidant capacity in living cells. Oxidative stress is implicated in a number of pathological conditions such as cardiovascular and neurodegenerative diseases but it also has crucial roles in the regulation of cellular activities. Over the last few decades, many studies have identified significant connections between oxidative stress, inflammation and healing. In particular, increasing evidence indicates that the production of oxidants and the cellular response to oxidative stress are intricately connected to the fate of implanted biomaterials. This review article provides an overview of the major mechanisms underlying the link between oxidative stress and the biocompatibility of biomaterials. ROS, RNS and lipid peroxidation products act as chemo-attractants, signalling molecules and agents of degradation during the inflammation and healing phases. As chemo-attractants and signalling molecules, they contribute to the recruitment and activation of inflammatory and healing cells, which in turn produce more oxidants. As agents of degradation, they contribute to the maturation of the extracellular matrix at the healing site and to the degradation of the implanted material. Oxidative stress is itself influenced by the material properties, such as by their composition, their surface properties and their degradation products. Because both cells and materials produce and react with oxidants, oxidative stress may be the most direct route mediating the communication between cells and materials. Improved understanding of the oxidative stress mechanisms following biomaterial implantation may therefore help the development of new biomaterials with enhanced biocompatibility

Identification of low order models for large scale processes

Many industrial chemical processes are complex, multi-phase and large scale in nature. These processes are characterized by various nonlinear physiochemical effects and fluid flows. Such processes often show coexistence of fast and slow dynamics during their time evolutions. The increasing demand for a flexible operation of a complex process, a pressing need to improve the product quality, an increasing energy cost and tightening environmental regulations make it rewarding to automate a large scale manufacturing process. Mathematical tools used for process modeling, simulation and control are useful to meet these challenges. Towards this purpose, development of process models, either from the first principles (conservation laws) i.e. the rigorous models or the input-output data based models constitute an important step. Both types of models have their own advantages and pitfalls. Rigorous process models can approximate the process behavior reasonably well. The ability to extrapolate the rigorous process models and the physical interpretation of their states make them more attractive for the automation purpose over the input-output data based identified models. Therefore, the use of rigorous process models and rigorous model based predictive control (R-MPC) for the purpose of online control and optimization of a process is very promising. However, due to several limitations e.g. slow computation speed and the high modeling efforts, it becomes difficult to employ the rigorous models in practise. This thesis work aims to develop a methodology which will result in smaller, less complex and computationally efficient process models from the rigorous process models which can be used in real time for online control and dynamic optimization of the industrial processes. Such methodology is commonly referred to as a methodology of Model (order) Reduction. Model order reduction aims at removing the model redundancy from the rigorous process models. The model order reduction methods that are investigated in this thesis, are applied to two benchmark examples, an industrial glass manufacturing process and a tubular reactor. The complex, nonlinear, multi-phase fluid flow that is observed in a glass manufacturing process offers multiple challenges to any model reduction technique. Often, the rigorous first principle models of these benchmark examples are implemented in a discretized form of partial differential equations and their solutions are computed using the Computational Fluid Dynamics (CFD) numerical tools. Although these models are reliable representations of the underlying process, computation of their dynamic solutions require a significant computation efforts in the form of CPU power and simulation time. The glass manufacturing process involves a large furnace whose walls wear out due to the high process temperature and aggressive nature of the molten glass. It is shown here that the wearing of a glass furnace walls result in change of flow patterns of the molten glass inside the furnace. Therefore it is also desired from the reduced order model to approximate the process behavior under the influence of changes in the process parameters. In this thesis the problem of change in flow patterns as result of changes in the geometric parameter is treated as a bifurcation phenomenon. Such bifurcations exhibited by the full order model are detected using a novel framework of reduced order models and hybrid detection mechanisms. The reduced order models are obtained using the methods explained in the subsequent paragraphs. The model reduction techniques investigated in this thesis are based on the concept of Proper Orthogonal Decompositions (POD) of the process measurements or the simulation data. The POD method of model reduction involves spectral decomposition of system solutions and results into arranging the spatio-temporal data in an order of increasing importance. The spectral decomposition results into spatial and temporal patterns. Spatial patterns are often known as POD basis while the temporal patterns are known as the POD modal coefficients. Dominant spatio-temporal patterns are then chosen to construct the most relevant lower dimensional subspace. The subsequent step involves a Galerkin projection of the governing equations of a full order first principle model on the resulting lower dimensional subspace. This thesis can be viewed as a contribution towards developing the databased nonlinear model reduction technique for large scale processes. The major contribution of this thesis is presented in the form of two novel identification based approaches to model order reduction. The methods proposed here are based on the state information of a full order model and result into linear and nonlinear reduced order models. Similar to the POD method explained in the previous paragraph, the first step of the proposed identification based methods involve spectral decomposition. The second step is different and does not involve the Galerkin projection of the equation residuals. Instead, the second step involves identification of reduced order models to approximate the evolution of POD modal coefficients. Towards this purpose, two different methods are presented. The first method involves identification of locally valid linear models to represent the dynamic behavior of the modal coefficients. Global behavior is then represented by ‘blending’ the local models. The second method involves direct identification of the nonlinear models to represent dynamic evolution of the model coefficients. In the first proposed model reduction method, the POD modal coefficients, are treated as outputs of an unknown reduced order model that is to be identified. Using the tools from the field of system identification, a blackbox reduced order model is then identified as a linear map between the plant inputs and the modal coefficients. Using this method, multiple local reduced LTI models corresponding to various working points of the process are identified. The working points cover the nonlinear operation range of the process which describes the global process behavior. These reduced LTI models are then blended into a single Reduced Order-Linear Parameter Varying (ROLPV) model. The weighted blending is based on nonlinear splines whose coefficients are estimated using the state information of the full order model. Along with the process nonlinearity, the nonlinearity arising due to the wear of the furnace wall is also approximated using the RO-LPV modeling framework. The second model reduction method that is proposed in this thesis allows approximation of a full order nonlinear model by various (linear or nonlinear) model structures. It is observed in this thesis, that, for certain class of full order models, the POD modal coefficients can be viewed as the states of the reduced order model. This knowledge is further used to approximate the dynamic behavior of the POD modal coefficients. In particular, reduced order nonlinear models in the form of tensorial (multi-variable polynomial) systems are identified. In the view of these nonlinear tensorial models, the stability and dissipativity of these models is investigated. During the identification of the reduced order models, the physical interpretation of the states of the full order rigorous model is preserved. Due to the smaller dimension and the reduced complexity, the reduced order models are computationally very efficient. The smaller computation time allows them to be used for online control and optimization of the process plant. The possibility of inferring reduced order models from the state information of a full order model alone i.e. the possibility to infer the reduced order models in the absence of access to the governing equations of a full order model (as observed for many commercial software packages) make the methods presented here attractive. The resulting reduced order models need further system theoretic analysis in order to estimate the model quality with respect to their usage in an online controller setting