Traditional Blocking Research Articles

Exploring Locality Sensitive Hashing as a Blocking Method for Large-Scale Administrative Datasets

ObjectivesLinking large-scale datasets is challenging due to the computational power required. This research explores using Locality-Sensitive-Hashing (LSH) as a novel blocking method to facilitate linking large administrative data. LSH uses an efficient technique of hashing data while preserving similarity, reducing the search space and processing power required to find links. ApproachSamples were taken from a gold-standard dataset and blocked using LSH. Various parameters were tested to establish LSH’s optimal performance at retrieving linked pairs and reducing the search space size. ResultsTesting the method on small datasets gave promising results, with the LSH method creating ~9,000 candidate pairs. This is a improvement over our traditional blocking method or cartesian product, which created ~70,000 and 23.4 million candidate pairs, respectively. We have therefore shown that LSH can significantly reduce the search-space size. A further advantage of the method is its capability in handling alternative variables such as those that may be present in longitudinal or composite data, without needing to manually anticipate the different combinations of variables which may occur. Variable formats to simulate agreement weighting were also considered, with encouraging results. Conclusion and ImplicationsOur research shows that LSH can be used to drastically reduce the search space when blocking for data linkage This suggests developing LSH as a blocking method could result in more effective blocking, quicker linkage, and no loss of quality compared to traditional methods. However, further research on increasing the method’s scale is necessary.

Comparison of Edge of Lamina Block with Thoracic Paravertebral Block and Retrolaminar Block for Analgesic Efficacy in Adult Patients Undergoing Video-Assisted Thoracic Surgery: A Prospective Randomized Study.

A novel ultrasound-guided paravertebral block, the edge laminar block (ELB) was reported recently. However, it was unclear how effective ELB was in comparison with traditional blocking methods. We conducted a trial to compare the analgesic efficacy of ELB with the thoracic paravertebral block (TPVB) and the retrolaminar block (RLB) in patients undergoing video-assisted thoracic surgery (VATS). We identified 90 patients who were scheduled for VATS and randomly assigned them to three groups: ELB group (Group E), TPVB group (Group T), and RLB group (Group R). Each group underwent ELB, TPVB, and RLB, respectively, under ultrasound guidance before general anesthesia induction. All patients received post-operative routine analgesia protocol. Our primary outcome was the extent of dermatomal sensory loss on the midclavicular, midaxillary, and scapular lines, measured using a pinprick 15 minutes after the nerve block. Secondary outcomes included the intraoperative dose of sufentanil, the numerical rating scale (NRS) scores assessed in the post-anesthesia care unit (PACU) and at 6, 12, and 24 hours post-operatively, and pethidine administrated as analgesic rescue dose. The percentages of nerve block range reaching the midclavicular line, midaxillary line, and scapular line in Group E were 96.7%, 93.3%, 93.3%, and 60% in Group T and 30%, 56.7%, and 96.7% in Group R, respectively. Group E had wider dermatomal sensory loss on the midclavicular line and midaxillary line compared to Group R (P < 0.001) and had a wider range compared to Group T on the scapular line (P < 0.001). There was no significant difference in the intraoperative use of sufentanil in the three groups. Post-operative NRS scores at each time point were significantly lower in Group E than those in the other two groups (P < 0.01). ELB had a wider nerve block range and applied better post-operative analgesia in comparison with TPVB and RLB.

Abstract 3538: A CTLA-4 targeted ETB for Treg depletion shows favorable preclinical efficacy and safety

Abstract CTLA-4-targeted engineered toxin bodies (ETBs) are designed to deplete immune-suppressive regulatory T cells (Tregs) in the tumor microenvironment (TME) directly and in a manner independent of Fc-mediated effector functions, offering a unique approach to CTLA-4 targeted therapy that may show efficacy where blocking antibodies have failed. ETBs are large molecule proteins consisting of an antibody fragment genetically fused to a proprietary de-immunized (DI) form of the Shiga-like toxin-1 subunit A (SLTA). Once engaged to the specific cell surface target of interest, ETBs internalize, route to the cytosol, and permanently inactivate ribosomes through an irreversible enzymatic process. This results in cell death via apoptotic mechanisms. Here we describe the preclinical characterization of a candidate CTLA-4-targeted ETB. The candidate ETB kills gain-of-function model cell lines that express CTLA-4 at levels observed on primary human tumor Tregs. Notably, the potency of the candidate ETB is limited when CTLA-4 is expressed at low levels, thereby allowing a mechanism to spare CD8+ cytotoxic T lymphocytes (CTLs) and effector CD4+ T cells from targeted depletion. The candidate ETB binds CTLA-4 with high affinity utilizing a unique biparatopic binding domain. Using in vitro systems, we show that the candidate ETB can block CTLA-4:B7 interactions and repress Treg-mediated T-cell suppression. In a syngeneic mouse pharmacodynamic model, the candidate ETB depletes tumor-resident Tregs and increases the CD8:Treg ratio in the TME. A toxicological study in naive NHP demonstrate that the candidate ETB, which is cross-reactive to NHP, is well-tolerated at 450 mcg/kg (highest dose tested) administered intravenously once weekly for 4-weeks and does not significantly alter the CTLA-4 low/null peripheral T cell populations. Overall, the candidate ETB is a unique CTLA-4 targeting modality that is designed to deplete Tregs selectively and directly in the tumor, spare CD8+ CTLs, and represents an alternative treatment option to traditional blocking antibodies. Development activities are underway to support the upcoming IND application. Citation Format: Asis Sarkar, Rebecca Martin, Lauren R. Byrne, Caleigh Howard, Swati Khanna, Lilia A. Rabia, Diana Adhikari, Michaela M. Sousares, Alvaro Aldana, Garrett L. Robinson, Jay Zhao, Chris B. Moore, Aimee Iberg. A CTLA-4 targeted ETB for Treg depletion shows favorable preclinical efficacy and safety [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3538.

Lattice-based progressive author disambiguation

Different use cases have acknowledged the importance of author identities and the non-triviality of determining them. Author disambiguation (AD) is a special case of entity resolution resolving author mentions to actual real-world authors. Like in other entity resolution tasks, AD methods are strongly restricted by scale and person name conventions. So far, this has been addressed by static blocking methods which cannot adapt to such collection-dependent properties. We address this gap by presenting the first progressive method of author disambiguation. Progressive entity resolution tackles large-scale conflation problems by repeatedly increasing the number of pairs compared for potential equivalence. Our method uses lattice structures to model name inclusion in an adaptive and more efficient way than traditional blocking techniques based on alphabetical order or fixed-level generalization. Our work offers additional insights into the relationship between name-matching, different blocking schemes, blocking and clustering as well as cost and benefit. Using the Web of Science as large-scale annotated test data, we observe and compare our model’s performance over time and compare it with various configurations and baselines. Our approach consistently outperforms state-of-the-art blocking methods, underlining its contribution to the field of author disambiguation. Our approach offers a novel alternative for tackling ambiguity in entity resolution, which is a major challenge for many information systems.

Performance Analysis of RCU-Style Non-Blocking Synchronization Mechanisms on a Manycore-Based Operating System

There have been recent advances in multi-core machines with tens and hundreds of cores, and there is an increasing emphasis on the software structure. Many different synchronization mechanism techniques have been developed to improve the performance and the scalability of manycore systems. As the non-blocking algorithms are promising in overcoming performance limits in traditional lock-based blocking synchronization mechanisms, we are observing an increased usage ratio and a number of non-blocking synchronization algorithms. For example, the usage ratio of RCU increased sharply in recent years. Since RCU exhibits low write performance and is difficult to use, the research community introduced RLU and MV-RLU synchronization algorithms to address the issues. RLU and MV-RLU, which are called RCU-style synchronization mechanisms, are promising in terms of providing easy-to-use APIs (Application Programming Interfaces) and better performance in manycore machines. To expand the applicability of RCU-style mechanisms, we need to measure the performance and analyze their measurements in various environments. To meet the goal, we evaluate them at the user and kernel level in sv6 variant, which is a research operating system on a manycore system. In order to enable RCU-style synchronization algorithms in sv6 variant, we implemented and modified some of the libraries and memory allocators in sv6 variant. We use micro-benchmarks that exploit a linked list and hash table to measure the performance while experimenting with parameters of the benchmarks and types of data structures. In most of the experiments, we observed that MV-RLU is scalable. MV-RLU exhibits about thirteen times better throughput than RCU in the case of running 70 threads. In addition, we compare the operation procedures and APIs of each RCU-style synchronization algorithm to analyze the pros and cons of the algorithms.

Towards Double Defense Network Security Based on Multi-Identifier Network Architecture.

Recently, more and more mobile devices have been connected to the Internet. The Internet environment is complicated, and network security incidents emerge endlessly. Traditional blocking and killing passive defense measures cannot fundamentally meet the network security requirements. Inspired by the heuristic establishment of multiple lines of defense in immunology, we designed and prototyped a Double Defense strategy with Endogenous Safety and Security (DDESS) based on multi-identifier network (MIN) architecture. DDESS adopts the idea of a zero-trust network, with identity authentication as the core for access control, which solves security problems of traditional IP networks. In addition, DDESS achieves individual static security defense through encryption and decryption, consortium blockchain, trusted computing whitelist, and remote attestation strategies. At the same time, with the dynamic collection of data traffic and access logs, as well as the understanding and prediction of the situation, DDESS can realize the situation awareness of network security and the cultivation of immune vaccines against unknown network attacks, thus achieving the active herd defense of network security.

Reconstructing winter climate anomalies in the Euro-Atlantic sector using circulation patterns

Abstract. The efficacy of Euro-Atlantic circulation regimes for estimating wintertime climate anomalies (precipitation and surface temperature) over Europe is assessed. A comparison of seasonal climate reconstructions from two different regime frameworks (cluster analysis of the low-level zonal flow, and traditional blocking indices) is presented and contrasted with seasonal reconstructions using the North Atlantic Oscillation (NAO) index. The reconstructions are quantitatively evaluated using correlations and the coefficient of efficiency, accounting for misfit in phase and amplitude. The skill of the various classifications in reconstructing seasonal anomalies depends on the variable and region of interest. The jet and blocking regimes are found to capture more spatial structure in seasonal precipitation anomalies over Europe than the NAO, with the jet framework showing generally better skill relative to the blocking indices. The reconstructions of temperature anomalies have lower skill than those for precipitation, with the best results for temperature obtained by the NAO for high-latitude and by the blocking framework for southern Europe. All methods underestimate the magnitude of seasonal anomalies due to the large variability in precipitation and temperature within each classification pattern.

Production scheduling for blocking flowshop in distributed environment using effective heuristics and iterated greedy algorithm

• We study a distributed blocking flowshop scheduling problem. • We present six effective constructive heuristics and an iterated greedy algorithm . • Two different destructions focusing on the problem characteristic are designed. • Three local search procedures based on the various neighbourhood are presented. • The computational experiments prove the effectiveness of presented algorithms. Production scheduling plays an important role in the intelligent decision support system and intelligent optimization decision technology. In the context of the globalization trend, the current production and management may extend from a single factory to a distributed production network. In this paper, we study the distributed blocking flowshop scheduling problem (DBFSP) that is an important generalization of the traditional blocking flowshop scheduling problem in the distributed environment. Six constructive heuristics and an iterated greedy (IG) algorithm are proposed to minimize the makespan, which provides procedures for obtaining efficient and effective solutions to make decision-making sounder. The first five heuristics are developed based on the well-known NEH2 heuristic [B. Naderi, R. Ruiz, The distributed permutation flowshop scheduling problem, Computers & Operations Research, 37 (4) (2010) 754–768.] and the last heuristic is presented by extending the PW heuristic [Q.K. Pan, L. Wang, Effective heuristics for the blocking flowshop scheduling problem with makespan minimization, Omega, 40 (2) (2012) 218–229.] to DBFSP in an effective way. The composite heuristics that combining constructive heuristics and local searches are also studied. The proposed composite heuristics are chosen to generate an initial solution with a high level of quality. Keeping the simplicity of the IG algorithm, three local search procedures, two destruction procedures, an improved reconstruction procedure, and a simulated annealing-like acceptance criterion are well designed based on the problem-specific knowledge to enhance the IG algorithm. The computational experiments are carried out based on the 720 benchmark instances from the literature. The results show that the proposed heuristics are very effective for solving the problem under consideration and the presented IG algorithm performs significantly better than the other state-of-the-art metaheuristics from the literature.

Efficient and effective ER with progressive blocking

Blocking is a mechanism to improve the efficiency of Entity Resolution (ER) which aims to quickly prune out all non-matching record pairs. However, depending on the distributions of entity cluster sizes, existing techniques can be either (a) too aggressive, such that they help scale but can adversely affect the ER effectiveness, or (b) too permissive, potentially harming ER efficiency. In this paper, we propose a new methodology of progressive blocking (pBlocking) to enable both efficient and effective ER, which works seamlessly across different entity cluster size distributions. pBlocking is based on the insight that the effectiveness-efficiency trade-off is revealed only when the output of ER starts to be available. Hence, pBlocking leverages partial ER output in a feedback loop to refine the blocking result in a data-driven fashion. Specifically, we bootstrap pBlocking with traditional blocking methods and progressively improve the building and scoring of blocks until we get the desired trade-off, leveraging a limited amount of ER results as a guidance at every round. We formally prove that pBlocking converges efficiently ($O(n log^2 n)$ time complexity, where n is the total number of records). Our experiments show that incorporating partial ER output in a feedback loop can improve the efficiency and effectiveness of blocking by 5x and 60% respectively, improving the overall F-score of the entire ER process up to 60%.

Amplifying and Leveraging Generated Force Upon Heating and Cooling in SMA Knitted Actuators.

This work reexamines traditional shape memory alloy (SMA) loading paths commonly used in SMA-based actuator applications and presents a novel, superimposed condition in which SMA generates substantial forces upon heating and cooling. This atypical effect, which is investigated with a textile-based actuator, was found to be prominent at the completion of material phase transformation, at which point thermal expansion/contraction became the dominant force-generating mechanism. We demonstrate that amplification of generated forces can be accomplished by varying the applied thermal load, applied structural strain, as well as actuator architecture. Specifically, we present SMA knitted actuators as an actuator architecture that increases the effect by aggregating SMA wires within a complex strain profile-effectively providing a larger operational window for the effect to propagate. The amplification of blocking forces through this novel operational procedure suggests reconsidering traditional blocking force design paradigms and opens untapped actuator application spaces, such as the highlighted medical and aerospace wearable technologies.

Blocking Techniques for Entity Linkage: A Semantics-Based Approach

Nowadays, data integration must often manage noisy data, also containing attribute values written in natural language such as product descriptions or book reviews. In the data integration process, Entity Linkage has the role of identifying records that contain information referring to the same object. Modern Entity Linkage methods, in order to reduce the dimension of the problem, partition the initial search space into “blocks” of records that can be considered similar according to some metrics, comparing then only the records belonging to the same block and thus greatly reducing the overall complexity of the algorithm. In this paper, we propose two automatic blocking strategies that, differently from the traditional methods, aim at capturing the semantic properties of data by means of recent deep learning frameworks. Both methods, in a first phase, exploit recent research on tuple and sentence embeddings to transform the database records into real-valued vectors; in a second phase, to arrange the tuples inside the blocks, one of them adopts approximate nearest neighbourhood algorithms, while the other one uses dimensionality reduction techniques combined with clustering algorithms. We train our blocking models on an external, independent corpus, and then, we directly apply them to new datasets in an unsupervised fashion. Our choice is motivated by the fact that, in most data integration scenarios, no training data are actually available. We tested our systems on six popular datasets and compared their performances against five traditional blocking algorithms. The test results demonstrated that our deep-learning-based blocking solutions outperform standard blocking algorithms, especially on textual and noisy data.

Achieve Global Renewable Energy and Low Carbon Goal Through Constructing Human 'Faults' in Electric Grids

The current 25% of global renewable energy among electric grids generation is far from the stated goal of 60% in 2040 and 100% in 2050. One of the biggest dilemma to cost-effectively increase this percentage, through grids interconnection, is that the traditional fault blocking methodology has reached its safety protection ceiling. In contrary to the traditional wisdom that any form of faults must be avoided, here we proposed a fault dredging methodology by constructing human “faults” to create artificial fault current release channels to solve high-level fault current bottleneck during grids interconnection. Using actual region electric grids from globally representative countries across six continents for demonstration, our results show that human “faults” can enable the percentages of renewable energy in USA, UK, China, Brazil, Australia, Nigeria and IEEE RTS system increase from 19.2%~47.2% to over 90%, which are much higher than all of conventional technologies. Correspondingly, the emissions of CO2, NOX and SOX in these systems respectively decrease by 67.84%~88.07%, 61.17%~88.24% and 58.25%~88.33%. Our findings suggest a new approach to reach the global renewable energy goal and promote the progress toward achieving carbon-free energy system.

A Survey on Blocking Technology of Entity Resolution

Entity resolution (ER) is a significant task in data integration, which aims to detect all entity profiles that correspond to the same real-world entity. Due to its inherently quadratic complexity, blocking was proposed to ameliorate ER, and it offers an approximate solution which clusters similar entity profiles into blocks so that it suffices to perform pairwise comparisons inside each block in order to reduce the computational cost of ER. This paper presents a comprehensive survey on existing blocking technologies. We summarize and analyze all classic blocking methods with emphasis on different blocking construction and optimization techniques. We find that traditional blocking ER methods which depend on the fixed schema may not work in the context of highly heterogeneous information spaces. How to use schema information flexibly is of great significance to efficiently process data with the new features of this era. Machine learning is an important tool for ER, but end-to-end and efficient machine learning methods still need to be explored. We also sum up and provide the most promising trend for future work from the directions of real-time blocking ER, incremental blocking ER, deep learning with ER, etc.

A co-evolutionary genetic algorithm for the two-machine flow shop group scheduling problem with job-related blocking and transportation times

This study investigates a new two-machine flow shop group scheduling problem with job-related blocking and transportation times, which is derived from the realistic pipe-making process of steel pipe products in the modern steel manufacturing industry. In contrast to the traditional blocking constraint, the attributes of jobs, not the quantity of jobs in the buffer area, are used to determine the need for a blocking feature. The objective is to minimize the makespan. We present a mixed integer linear programming model and prove that the problem is strongly NP-hard. As the problem is a joint decision of two sub-problems, namely group scheduling and job scheduling within each group, a co-evolutionary genetic algorithm (CGA) is proposed to solve it. In the proposed CGA, the two sub-problems are synergistically evolved via a co-evolutionary framework. A block-mining-based artificial chromosome construction strategy is designed to speed up the convergence process. Computational experiments based on actual production data are carried out. The results indicate that the proposed CGA is effective for the considered problem.

Spectral element applications in complex nuclear reactor geometries: Tet-to-hex meshing

The spectral element code Nek5000 is an open-source, higher-order computational fluid dynamics code developed at Argonne National Laboratory. It is designed to solve incompressible Navier-Stokes equations, but it also has a low-Mach-number approximation feature available. Large eddy simulation is approached by explicit filtering of the velocity field (and other fields) to mimic the effect of dissipation due to the unresolved scale. The computational domain is decomposed into second-order hexahedral elements that conform to the boundaries. However, generating a high-quality pure-hexahedral mesh can be challenging for some problems. For simple geometries, traditional blocking methods can be used to decompose the domain into smaller blocks to generate a so-called structural mesh. A structural mesh can maintain good orthogonality but can have a highly skewed mesh to conform to the geometry, as well as unnecessary refinement in the far field. Moreover, for geometries with relative complexity, blocking the geometry becomes impossible. To address these issues, we adopted a tet-to-hex strategy to generate a pure hexahedral mesh for Nek5000. First, we generate a pure tetrahedral mesh for the geometry; then we divide one tetrahedral element into four hexahedral elements. A pure tetrahedral mesh could be easily generated for complex geometries by using many current meshing codes. In this paper, we use the commercial codes ANSYS meshing and ANSYS ICEM to generate the pure tetrahedral mesh and then convert it to a pure hexahedral mesh. Boundary layers are extruded in ANSYSICEM to maintain near-wall resolution.

PROTOCOL OF THE HYPERTENSION THERAPY WITH IRBESARTAN VERSUS EPLERENONE FOR OBESE HYPERTENSIVE PATIENTS (HEBRO)

Objective: Hypertension and obesity often coexist and activation of the renin-angiotensin- aldosterone (RAS) axis plays a major pathophysiological role. Since obese hypertensive patients are less frequently controlled within-goal compared with hypertensives without obesity, we aimed to determine whether RAS blocking at the level of aldosterone (eplerenone) is accompanied by better blood pressure (BP) efficacy compared with the traditional blocking at an intermediate axis level (irbesartan). Design and method: A multicentre Hellenic-ESH Excellence centres setting was chosen and a parallel open-label, randomized-controlled, phase IV trial was projected to demonstrate two-sided superiority between comparator-drugs over a period 24-weeks. We include obese (body mass index from 30 to less than 40, aged 30–75 years), newly diagnosed or drug-naive patients with mild to moderate hypertension, confirmed by ambulatory BP monitoring (ABPM). Randomization (1:1) to either eplerenone or irbesartan will be pursued taking into account age, gender, baseline office systolic BP levels and renal function. At eight, sixteen and twenty-four weeks, ABPM will demonstrate BP control, and a stepped-care approach with the addition of amlodipine and indapamide will be followed in those uncontrolled during the two intermediate trial time frames. Renal function, electrolyte disorders, fatal and non-fatal cardiovascular events and adverse events will be also evaluated. A sample size of 336 patients (allowing 15% dropouts) will confer 80% power to detect a 4 mmHg ABPM averaged systolic BP difference. The primary endpoint is the 24-h systolic BP difference at 24 weeks. Secondary endpoints: 1) the change in office BP at 24 weeks;2) the intermediate change in 24-h systolic BP;3) the change in left ventricular mass and microalbuminuria at 24 weeks; 4) discontinuations from treatment because of adverse events. Trial registration: Clinicaltrials.gov NCT03476616;EudraCT: 2018-004809-58. Results: Ethics approval has been obtained from the hospital ethic committee board and the Hellenic National Organization for Medicines. The study will initiate in May 2019. Conclusions: Preliminary data are expected in 2020–2021 and the final results are anticipated in 2022. Randomized evidence to support further large-scale trials will be provided to refine research in this field. Findings will be published in peer-reviewed medical journals.

Comment on " In Vivo [18F]GE-179 Brain Signal Does Not Show NMDA-Specific Modulation with Drug Challenges in Rodents and Nonhuman Primates".

Schoenberger and colleagues ( Schoenberger et al. ( 2018 ) ACS Chem. Neurosci. 9 , 298 - 305 ) recently reported attempts to demonstrate specific binding of the positron emission tomography (PET) radiotracer, [18F]GE-179, to NMDA receptors in both rats and Rhesus macaques. GE-179 did not work as expected in animal models; however, we disagree with the authors' conclusion that "the [18F]GE-179 signal seems to be largely nonspecific". It is extremely challenging to demonstrate specific binding for the use-dependent NMDA receptor intrachannel ligands such as [18F]GE-179 in animals via traditional blocking, due to its low availability of target sites ( Bmax'). Schoenberger and colleagues anesthetized rats and Rhesus monkeys using isoflurane, which has an inhibitory effect on NMDA receptor function and thus would be expected to further reduce the Bmax'. The extent of glutamate release achieved in the provocation experiments is uncertain, as is whether a significant increase in NMDA receptor channel opening can be expected under anesthesia. Prior data suggest that the uptake of disubstituted arylguanidine-based ligands such as GE-179 can be reduced by phencyclidine binding site antagonists, if injection is performed in the absence of ketamine and isoflurane anesthesia, e.g., with GE-179's antecedent, CNS 5161 ( Biegon et al. ( 2007 ) Synapse 61 , 577 - 586 ), and with GMOM ( van der Doef et al. ( 2016 ) J. Cereb. Blood Flow Metab. 36 , 1111 - 1121 ). However, the extent of nonspecific uptake remains uncertain.

AHAB: Aligning heterogeneous knowledge bases via iterative blocking

With the development of information extraction, there have been an increasing number of large-scale knowledge bases available in different domains. In recent years, a great deal of approaches have been proposed for large-scale knowledge base alignment. Most of them are based on iterative matching. If a pair of entities has been aligned, their compatible neighbors are selected as candidate entity pairs. The limitation of these methods is that they discover candidate entity pairs depending on aligned relations, which cannot be used for aligning heterogeneous knowledge bases. Only few existing methods focus on aligning heterogeneous knowledge bases, which discover candidate entity pairs just for once by traditional blocking methods. However, the performance of these methods depends on blocking keys heavily, which are hard to select. In this paper, we present an approach for aligning heterogeneous knowledge bases via iterative blocking (AHAB) to improve the discovery and refinement of candidate entity pairs. AHAB iteratively utilizes different relations for blocking, and then matches block pairs based on matched entity pairs. The Cartesian product of unmatched entities in matched block pairs forms candidate entity pairs. By filtering out dissimilar candidate entity pairs, matched entity pairs will be found. The number of matched entity pairs proliferates with iterations, which in turn helps match block pairs in each iteration. Experiments on real-world heterogeneous knowledge bases demonstrate that AHAB is able to yield a competitive performance.

A Type-Based Blocking Technique for Efficient Entity Resolution over Large-Scale Data

In data integration, entity resolution is an important technique to improve data quality. Existing researches typically assume that the target dataset only contain string-type data and use single similarity metric. For larger high-dimensional dataset, redundant information needs to be verified using traditional blocking or windowing techniques. In this work, we propose a novel ER-resolving method using a hybrid approach, including type-based multiblocks, varying window size, and more flexible similarity metrics. In our new ER workflow, we reduce the searching space for entity pairs by the constraint of redundant attributes and matching likelihood. We develop a reference implementation of our proposed approach and validate its performance using real-life dataset from one Internet of Things project. We evaluate the data processing system using five standard metrics including effectiveness, efficiency, accuracy, recall, and precision. Experimental results indicate that the proposed approach could be a promising alternative for entity resolution and could be feasibly applied in real-world data cleaning for large datasets.

Record linkage and deduplication using traditional blocking

Record Linkage and Deduplication are the two process that are used in matching records. Matching of records is done to remove the duplicate records. These duplicate records highly influence the outputs of data mining and data processing. If the matching of records is done on the single database, it is called Deduplication. In Deduplication we check for the duplicate records in the single database. Unlike deduplication if the matching of the records is done on the several databases it is called as record linkage. In this paper we also discuss about the indexing technique called as traditional blocking which is used to remove non matching pairs that leads to the less number of record pair to be compared.