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Trummer, Immanuel, i Christoph Koch. "Multi-objective parametric query optimization". Communications of the ACM 60, nr 10 (25.09.2017): 81–89. http://dx.doi.org/10.1145/3068612.
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Trummer, Immanuel, i Christoph Koch. "Multi-objective parametric query optimization". Proceedings of the VLDB Endowment 8, nr 3 (listopad 2014): 221–32. http://dx.doi.org/10.14778/2735508.2735512.
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Trummer, Immanuel, i Christoph Koch. "Multi-Objective Parametric Query Optimization". ACM SIGMOD Record 45, nr 1 (2.06.2016): 24–31. http://dx.doi.org/10.1145/2949741.2949748.
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Trummer, Immanuel, i Christoph Koch. "Multi-objective parametric query optimization". VLDB Journal 26, nr 1 (18.08.2016): 107–24. http://dx.doi.org/10.1007/s00778-016-0439-0.
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Wang, Chenxiao, Zach Arani, Le Gruenwald, Laurent d'Orazio i Eleazar Leal. "Re-optimization for Multi-objective Cloud Database Query Processing using Machine Learning". International Journal of Database Management Systems 13, nr 1 (28.02.2021): 21–40. http://dx.doi.org/10.5121/ijdms.2021.13102.
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In cloud environments, hardware configurations, data usage, and workload allocations are continuously changing. These changes make it difficult for the query optimizer of a cloud database management system (DBMS) to select an optimal query execution plan (QEP). In order to optimize a query with a more accurate cost estimation, performing query re-optimizations during the query execution has been proposed in the literature. However, some of there-optimizations may not provide any performance gain in terms of query response time or monetary costs, which are the two optimization objectives for cloud databases, and may also have negative impacts on the performance due to their overheads. This raises the question of how to determine when are-optimization is beneficial. In this paper, we present a technique called ReOptML that uses machine learning to enable effective re-optimizations. This technique executes a query in stages, employs a machine learning model to predict whether a query re-optimization is beneficial after a stage is executed, and invokes the query optimizer to perform the re-optimization automatically. The experiments comparing ReOptML with existing query re-optimization algorithms show that ReOptML improves query response time from 13% to 35% for skew data and from 13% to 21% for uniform data, and improves monetary cost paid to cloud service providers from 17% to 35% on skewdata.
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Bansal, Rohit, Deepak Kumar i Sushil Kumar. "Multi-objective Multi-Join Query Optimization using Modified Grey Wolf Optimization". International Journal of Advanced Intelligence Paradigms 17, nr 1/2 (2020): 1. http://dx.doi.org/10.1504/ijaip.2020.10019251.
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Kumar, Deepak, Deepti Mehrotra i Rohit Bansal. "Query Optimization in Crowd-Sourcing Using Multi-Objective Ant Lion Optimizer". International Journal of Information Technology and Web Engineering 14, nr 4 (październik 2019): 50–63. http://dx.doi.org/10.4018/ijitwe.2019100103.
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Nowadays, query optimization is a biggest concern for crowd-sourcing systems, which are developed for relieving the user burden of dealing with the crowd. Initially, a user needs to submit a structured query language (SQL) based query and the system takes the responsibility of query compiling, generating an execution plan, and evaluating the crowd-sourcing market place. The input queries have several alternative execution plans and the difference in crowd-sourcing cost between the worst and best plans. In relational database systems, query optimization is essential for crowd-sourcing systems, which provides declarative query interfaces. Here, a multi-objective query optimization approach using an ant-lion optimizer was employed for declarative crowd-sourcing systems. It generates a query plan for developing a better balance between the latency and cost. The experimental outcome of the proposed methodology was validated on UCI automobile and Amazon Mechanical Turk (AMT) datasets. The proposed methodology saves 30%-40% of cost in crowd-sourcing query optimization compared to the existing methods.
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Kumar, Akshay, i T. V. Vijay Kumar. "A Multi-Objective Approach to Big Data View Materialization". International Journal of Knowledge and Systems Science 12, nr 2 (kwiecień 2021): 17–37. http://dx.doi.org/10.4018/ijkss.2021040102.
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Big data comprises voluminous and heterogeneous data that has a limited level of trustworthiness. This data is used to generate valuable information that can be used for decision making. However, decision making queries on Big data consume a lot of time for processing resulting in higher response times. For effective and efficient decision making, this response time needs to be reduced. View materialization has been used successfully to reduce the query response time in the context of a data warehouse. Selection of such views is a complex problem vis-à-vis Big data and is the focus of this paper. In this paper, the Big data view selection problem is formulated as a bi-objective optimization problem with the two objectives being the minimization of the query evaluation cost and the minimization of the update processing cost. Accordingly, a Big data view selection algorithm that selects Big data views for a given query workload, using the vector evaluated genetic algorithm, is proposed. The proposed algorithm aims to generate views that are able to reduce the response time of decision-making queries.
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Kumar, Akshay, i T. V. Vijay Kumar. "Multi-Objective Big Data View Materialization Using NSGA-III". International Journal of Decision Support System Technology 14, nr 1 (1.01.2022): 1–28. http://dx.doi.org/10.4018/ijdsst.311066.
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Present day applications process large amount of data that is being produced at brisk rate and is heterogeneous with levels of trustworthiness. This Big data largely consists of semi-structured and unstructured data, which needs to be processed in admissible time so that timely decisions are taken that benefit the organization and society. Such real time processing would require Big data view materialization that would enable faster and timely processing of decision making queries. Several algorithms exist for Big data view materialization. These algorithms aim to select Big data views that minimize the total query processing cost for the query workload. In literature, this problem has been articulated as a bi-objective optimization problem, which minimizes the query evaluation cost along with the update processing cost. This paper proposes to adapt the reference point based non-dominated sorting genetic algorithm, to design an NSGA-III based Big data view selection algorithm (BDVSANSGA-III) to address this bi-objective Big data view selection problem. Experimental results revealed that the proposed BDVSANSGA-III was able to compute diverse non-dominated Big data views and performed better than the existing algorithms..
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Sanchez-Gomez, Jesus M., Miguel A. Vega-Rodríguez i Carlos J. Pérez. "Sentiment-oriented query-focused text summarization addressed with a multi-objective optimization approach". Applied Soft Computing 113 (grudzień 2021): 107915. http://dx.doi.org/10.1016/j.asoc.2021.107915.
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Chang, Ray-I., Shu-Yu Lin i Yuhsin Hung. "Particle swarm optimization with query-based learning for multi-objective power contract problem". Expert Systems with Applications 39, nr 3 (luty 2012): 3116–26. http://dx.doi.org/10.1016/j.eswa.2011.08.175.
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Zhu, Lei, Chaoqun Zheng, Xu Lu, Zhiyong Cheng, Liqiang Nie i Huaxiang Zhang. "Efficient Multi-modal Hashing with Online Query Adaption for Multimedia Retrieval". ACM Transactions on Information Systems 40, nr 2 (30.04.2022): 1–36. http://dx.doi.org/10.1145/3477180.
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Multi-modal hashing supports efficient multimedia retrieval well. However, existing methods still suffer from two problems: (1) Fixed multi-modal fusion. They collaborate the multi-modal features with fixed weights for hash learning, which cannot adaptively capture the variations of online streaming multimedia contents. (2) Binary optimization challenge. To generate binary hash codes, existing methods adopt either two-step relaxed optimization that causes significant quantization errors or direct discrete optimization that consumes considerable computation and storage cost. To address these problems, we first propose a Supervised Multi-modal Hashing with Online Query-adaption method. A self-weighted fusion strategy is designed to adaptively preserve the multi-modal features into hash codes by exploiting their complementarity. Besides, the hash codes are efficiently learned with the supervision of pair-wise semantic labels to enhance their discriminative capability while avoiding the challenging symmetric similarity matrix factorization. Further, we propose an efficient Unsupervised Multi-modal Hashing with Online Query-adaption method with an adaptive multi-modal quantization strategy. The hash codes are directly learned without the reliance on the specific objective formulations. Finally, in both methods, we design a parameter-free online hashing module to adaptively capture query variations at the online retrieval stage. Experiments validate the superiority of our proposed methods.
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Li, Yanni, Yuping Wang, Peng Jiang i Zhensong Zhang. "Multi-objective optimization integration of query interfaces for the Deep Web based on attribute constraints". Data & Knowledge Engineering 86 (lipiec 2013): 38–60. http://dx.doi.org/10.1016/j.datak.2013.01.003.
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Liu, Weiyi, Kun Yue, Xiaodong Fu, Zidu Yin i Jin Li. "Multi-Objective Oriented Categorization Based on the Coalitional Game Theory". International Journal on Artificial Intelligence Tools 25, nr 03 (czerwiec 2016): 1650011. http://dx.doi.org/10.1142/s0218213016500111.
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Discovering different groups, or called classes, is useful for pattern recognition, data preprocessing, association analysis, query optimization, etc. To make every object satisfied as much as possible, the groups are generated by the associations or behaviors among participating objects other than the attributes owned by themselves. By mainly considering the mutual associations among the given objects and based on the game theory, in this paper we study the multi-objective oriented categorization. Based on the idea of Shapley value in the coalitional game, we first propose the concept of priority groups and give the efficient algorithm for computing the satisfaction degree of players in a group. Based on the idea of strategic games and Nash equilibrium, we then give the algorithm for computing an approximate equilibrium to solve the conflicts between the strategies of players, and consequently achieve the ultimate multi-objective oriented groups. Preliminary experiments and performance studies verify the efficiency and effectiveness of our methods.
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Molaei, Faezeh, i Shirin Ghatrehsamani. "Kinematic-Based Multi-Objective Design Optimization of a Grapevine Pruning Robotic Manipulator". AgriEngineering 4, nr 3 (4.07.2022): 606–25. http://dx.doi.org/10.3390/agriengineering4030040.
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Annual cane pruning of grape vineyards is a time-consuming and labor-intensive job, but no mechanized or automatic way has been developed to do it yet. Robotic pruning can be a perfect alternative to human labor. This article proposes a systematic seven-stage procedure to design a kinematically optimized manipulator, named ‘Prubot’, to manage vineyards’ cane pruning. The manipulator structure was chosen, resulting in a 7R (Revolute) manipulator with a spherical shoulder and wrist. To obtain the design constraints, the manipulator task space was modeled. The robot’s second and third link lengths were determined by optimizing the global translational version of the measure of manipulability and the measure of isotropy of the manipulator arm section. Finally, simulations confirmed the appropriateness of the manipulator workspace. Furthermore, sampling-based path planning simulations were carried out to evaluate the manipulator’s kinematic performance. Results illustrated the impressive kinematic performance of the robot in terms of path planning success rate (≅100%). The simulations also suggest that among the eight single-query sampling-based path planning algorithms used in the simulations, Lazy RRT and KPIECE are the best (≤5 s & ~100%) and worst ≥5 s &≤25% path planning algorithms for such a robot in terms of computation time and success rate, respectively. The procedure proposed in this paper offers a foundation for the kinematic and task-based design of a cane pruning manipulator. It could be promisingly used for designing similar agricultural manipulators.
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Tejera, Eduardo, Yunierkis Pérez-Castillo, Andrea Chamorro, Alejandro Cabrera-Andrade i Maria Eugenia Sanchez. "A Multi-Objective Approach for Drug Repurposing in Preeclampsia". Molecules 26, nr 4 (3.02.2021): 777. http://dx.doi.org/10.3390/molecules26040777.
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Preeclampsia is a hypertensive disorder that occurs during pregnancy. It is a complex disease with unknown pathogenesis and the leading cause of fetal and maternal mortality during pregnancy. Using all drugs currently under clinical trial for preeclampsia, we extracted all their possible targets from the DrugBank and ChEMBL databases and labeled them as “targets”. The proteins labeled as “off-targets” were extracted in the same way but while taking all antihypertensive drugs which are inhibitors of ACE and/or angiotensin receptor antagonist as query molecules. Classification models were obtained for each of the 55 total proteins (45 targets and 10 off-targets) using the TPOT pipeline optimization tool. The average accuracy of the models in predicting the external dataset for targets and off-targets was 0.830 and 0.850, respectively. The combinations of models maximizing their virtual screening performance were explored by combining the desirability function and genetic algorithms. The virtual screening performance metrics for the best model were: the Boltzmann-Enhanced Discrimination of ROC (BEDROC)α=160.9 = 0.258, the Enrichment Factor (EF)1% = 31.55 and the Area Under the Accumulation Curve (AUAC) = 0.831. The most relevant targets for preeclampsia were: AR, VDR, SLC6A2, NOS3 and CHRM4, while ABCG2, ERBB2, CES1 and REN led to the most relevant off-targets. A virtual screening of the DrugBank database identified estradiol, estriol, vitamins E and D, lynestrenol, mifrepristone, simvastatin, ambroxol, and some antibiotics and antiparasitics as drugs with potential application in the treatment of preeclampsia.
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Popescu, Claudiu, Lacrimioara Grama i Corneliu Rusu. "A Highly Scalable Method for Extractive Text Summarization Using Convex Optimization". Symmetry 13, nr 10 (30.09.2021): 1824. http://dx.doi.org/10.3390/sym13101824.
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The paper describes a convex optimization formulation of the extractive text summarization problem and a simple and scalable algorithm to solve it. The optimization program is constructed as a convex relaxation of an intuitive but computationally hard integer programming problem. The objective function is highly symmetric, being invariant under unitary transformations of the text representations. Another key idea is to replace the constraint on the number of sentences in the summary with a convex surrogate. For solving the program we have designed a specific projected gradient descent algorithm and analyzed its performance in terms of execution time and quality of the approximation. Using the datasets DUC 2005 and Cornell Newsroom Summarization Dataset, we have shown empirically that the algorithm can provide competitive results for single document summarization and multi-document query-based summarization. On the Cornell Newsroom Summarization Dataset, it ranked second among the unsupervised methods tested. For the more challenging task of multi-document query-based summarization, the method was tested on the DUC 2005 Dataset. Our algorithm surpassed the other reported methods with respect to the ROUGE-SU4 metric, and it was at less than 0.01 from the top performing algorithms with respect to ROUGE-1 and ROUGE-2 metrics.
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Meng, Kun, Chunyi Cui, Haijiang Li i Hailong Liu. "Ontology-Based Approach Supporting Multi-Objective Holistic Decision Making for Energy Pile System". Buildings 12, nr 2 (18.02.2022): 236. http://dx.doi.org/10.3390/buildings12020236.
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The traditional way of designing energy pile system is mostly single domain/objective oriented, which lacks of means to coherently consider different while relevant factors across domains. The cost for life cycle design, construction and maintenance, return of investment, CO2 emission related sustainable requirements, and so on also need to be considered, in a systematic manner, along with the main functional design objective for loading capacity and robustness. This paper presents a novel multi-objective holistic approach for energy pile system design using ontology based multi-domain knowledge orchestration, which can holistically provide the designers with across domain factors regarding financial, safety, and environmental impact, for smart and holistic consideration during the early design stage. A prototypical ontology-based decision tool has been developed, aiming at the holistic optimization for energy pile system by combining ontology and Semantic Web Rule Language rules. A case study was performed to illustrate the details on how to apply knowledge query to provide a series of design alternatives autonomously by taking different design parameters into account. The method has demonstrated its practicability and scientific feasibility, it also shows the potential to be adopted and extended for other domains when dealing with multi-objective holistic design making.
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Li, Mingzhen, Yunfeng Wang, Guangcan Yang, Shoushan Luo, Yang Xin, Hongliang Zhu, Yixian Yang, Yuling Chen i Fugui Luo. "DGS-HSA: A Dummy Generation Scheme Adopting Hierarchical Structure of the Address". Applied Sciences 10, nr 2 (11.01.2020): 548. http://dx.doi.org/10.3390/app10020548.
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With the increasing convenience of location-based services (LBSs), there have been growing concerns about the risk of privacy leakage. We show that existing techniques fail to defend against a statistical attack meant to infer the user’s location privacy and query privacy, which is due to continuous queries that the same user sends in the same location in a short time, causing the user’s real location to appear consecutively more than once and the query content to be the same or similar in the neighboring query. They also fail to consider the hierarchical structure of the address, so locations in an anonymous group may be located in the same organization, resulting in leaking of the user’s organization information and reducing the privacy protection effect. This paper presents a dummy generation scheme, considering the hierarchical structure of the address (DGS-HSA). In our scheme, we introduce a novel meshing method, which divides the historical location dataset according to the administrative region division. We also choose dummies from the historical location dataset with the two-level grid structure to realize the protection of the user’s location, organization information, and query privacy. Moreover, we prove the feasibility of the presented scheme by solving the multi-objective optimization problem and give the user’s privacy protection parameters recommendation settings, which balance the privacy protection level and system overhead. Finally, we evaluate the effectiveness and the correctness of the DGS-HSA through theoretical analysis and extensive simulations.
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Tatarenko, Tatiana, i Jan Zimmermann. "Distributed optimization methods for N-cluster games". at - Automatisierungstechnik 70, nr 3 (1.03.2022): 237–47. http://dx.doi.org/10.1515/auto-2021-0137.
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Abstract This work provides methodological approaches to solve convex optimization problems arising in multi-agent systems which can be reformulated in terms of a so called N-cluster game. We consider different settings of information available to each agent in the system. First, we present a centralized algorithm, which requires a central coordinator having full access to information about agents’ actions and gradients of their cost functions, to demonstrate how the standard gradient descent method can be applied to achieve an optimal output in N-cluster games. After that we relax the full information setting and assume that only partial information is available to each agent. Focus lies on the following two cases. In the first case, the agents have access to their gradient functions and are allowed to exchange information with their local neighbors over a communication graph that connects the whole system. In the second case, the agents do not know the functional form of their objectives/gradients and can only access the current values of their objective functions at some query point. Moreover, the agents are allowed to communicate only with their local neighbors within the cluster to which they belong. For both settings we present the convergent optimization procedures and analyse their efficiency in simulations.
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Chen, Yuzhong, Yang Yu i Guolong Chen. "Shortest distance estimation in large scale graphs". Engineering Computations 31, nr 8 (28.10.2014): 1635–47. http://dx.doi.org/10.1108/ec-11-2012-0286.
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Purpose – Shortest distance query between a pair of nodes in a graph is a classical problem with a wide variety of applications. Exact methods for this problem are infeasible for large-scale graphs such as social networks with hundreds of millions of users and links due to their high complexity of time and space. The purpose of this paper is to propose a novel landmark selection strategy which can estimate the shortest distances in large-scale graphs and clarify the efficiency and accuracy of the proposed strategy in comparison with currently used strategies. Design/methodology/approach – Different from existing strategies, the landmark selection problem is regarded as a binary combinational optimization problem consisting of two optimization objectives and one constraint. Further, the original binary combinational optimization problem with constraints is transformed to a proper form of optimization objectives without any additional constraints and the equivalence of solutions is proved. Finally the solution of the optimization problem is performed with a modified multi-objective particle swarm optimization (MOPSO) integrating the mutation operator and crossover operator of genetic algorithm. Findings – Four real networks of large scale are used as data sets to carry out the experiments and the experiment results show that the proposed strategy improves both of the accuracy and time efficiency to perform shortest distance estimation in large scale graph compared to other currently used strategies. Originality/value – This paper proposes a novel landmark selection strategy which regards the landmark selection problem as a binary combinational optimization problem. The original binary combinational optimization problem with constraints is transformed to a proper form of optimization objectives without constraints and the equivalence of these two optimization problems is proved. This novel strategy also utilizes a modified MOPSO integrating the mutation operator and crossover operator of genetic algorithm.
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Moskowitz, Craig H., Auayporn Nadamanee, Tamas Masszi, Edward Agura, Jerzy Holowiecki, Muneer H. Abidi, Andy I. Chen i in. "The Aethera Trial: Results of a Randomized, Double-Blind, Placebo-Controlled Phase 3 Study of Brentuximab Vedotin in the Treatment of Patients at Risk of Progression Following Autologous Stem Cell Transplant for Hodgkin Lymphoma". Blood 124, nr 21 (6.12.2014): 673. http://dx.doi.org/10.1182/blood.v124.21.673.673.
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Abstract Background For the past 20 years, high-dose therapy plus autologous stem cell transplant (ASCT) has been the standard of care for patients (pts) with chemosensitive relapsed/refractory Hodgkin lymphoma (HL), providing a cure for approximately 50% of pts (Sureda 2005). Despite optimization of salvage chemotherapy, supportive care, and pt selection, improvements in outcomes post-ASCT have plateaued, likely due to disease progression (PD) in pts with pre-salvage therapy risk factors. The majority of pts with multiple risk factors will progress post-ASCT and novel therapy is urgently needed. Brentuximab vedotin (ADCETRIS®) comprises an anti-CD30 antibody conjugated by a protease-cleavable linker to a microtubule-disrupting agent, monomethyl auristatin E (MMAE), and has an objective response rate of 75% in relapsed or refractory HL. The AETHERA trial was initiated to evaluate whether early treatment with brentuximab vedotin post-ASCT can prevent progression in pts with HL (ClinicalTrials.gov #NCT01100502). Methods The AETHERA trial is a phase 3, randomized, double-blind, placebo-controlled, multicenter study. Pts were enrolled in 1 of 3 high-risk categories: refractory to frontline therapy: 196 pts (60%), relapse <12 months after frontline therapy: 107 pts (33%), and relapse ≥12 months after frontline therapy with extranodal disease: 26 pts (8%). Pts were required to have obtained a CR, partial remission (PR), or stable disease (SD) to salvage therapy prior to ASCT. After ASCT, pts received brentuximab vedotin 1.8 mg/kg q3wk and best supportive care (BSC) or placebo and BSC for up to 16 cycles (approximately 12 months). Pts with PD were to discontinue study therapy and could request unblinding; these pts may have received subsequent brentuximab vedotin on another clinical trial or on-label in some regions. The primary endpoint is PFS per an independent review facility (IRF); additional endpoints include overall survival (OS) and safety/tolerability. Results A total of 329 pts were randomized at 78 sites in the United States and Europe. Of the 329 enrolled pts, 327 received study treatment. The median age was 32 years (range, 18–76) and 53% were male. The median number of prior systemic therapies (frontline and salvage) was 2 (range, 2–8); 47% of pts received more than 2 prior systemic therapies. Response to salvage therapy pre-ASCT was CR: 137 pts (42%), PR: 112 pts (34%), and stable disease (SD): 80 pts (24%). Prior to pre-ASCT salvage therapy, 106 pts (32%) had extranodal involvement and 87 pts (26%) had B symptoms. Prior to ASCT, 110 pts (33%) were PET negative, 116 pts (35%) were PET-positive and PET status was unknown for 103 pts (31%). Overall, 78% of pts had multiple risk factors for progression. All pts had completed or discontinued study treatment as of August 2013. The median number of treatment cycles was 15, and 159 pts (49%) received 16 cycles. Reasons for treatment discontinuation were: PD: 93 pts (28%), adverse event (AE): 61 pts (19%), patient decision: 15 pts (5%), and investigator decision: 1 pt (<1%). At the time of this analysis (June 2014), the median follow-up time from randomization was 24.4 mos (range, 0–43 mos). For the pooled study population at 24 mos, the Kaplan Meier estimates were 54% (95% CI: 47%, 60%) for PFS and 88% (95% CI: 84%, 91%) for OS. Of the 50 deaths, 8 occurred prior to PD. AEs of any grade in >15% of pts were peripheral sensory neuropathy (36%), upper respiratory tract infection (25%), neutropenia (24%), fatigue (21%), cough (19%), and pyrexia (17%). Grade 3 or higher AEs in ≥10 pts were neutropenia (20%), peripheral sensory neuropathy (6%), thrombocytopenia (3%), and peripheral motor neuropathy (3%). As assessed by a Standardised MedDRA Query, 144 pts (44%) had treatment-emergent events of peripheral neuropathy (PN). Grade 3 PN was reported for 23 pts (7%) and was mostly sensory; no Grade 4 events were observed. Resolution or at least 1 grade of improvement in PN has occurred in 80% of pts with neuropathy events; the median time to resolution or improvement was 11.7 weeks (range, 0.1–128.0 weeks). Conclusions Based on analysis of blinded pooled efficacy data, the estimated 2-year PFS rate was 54% and the estimated 2 year OS rate was 88%. The most common reason for treatment discontinuation was disease progression. The primary analysis for the study will be performed in September 2014 and unblinded efficacy and safety data will be publicly presented for the first time at the conference. Figure 1 Figure 1. Disclosures Moskowitz: Genentech: Research Funding; Merck: Research Funding; Seattle Genetics, Inc.: Consultancy, Research Funding. Off Label Use: Brentuximab vedotin is indicated in the US for treatment of patients with Hodgkin lymphoma after failure of autologous stem cell transplant or after failure of at least two prior multi-agent chemotherapy regimens in patients who are not ASCT candidates and for the treatment of patients with systemic anaplastic large cell lymphoma after failure of at least one prior multi-agent chemotherapy regimen. Nadamanee:Gilead: Consultancy; Celgene: Consultancy; Spectrum: Research Funding; Seattle Genetics, Inc.: Research Funding. Masszi:Seattle Genetics, Inc.: Research Funding. Agura:Seattle Genetics, Inc.: Research Funding. Holowiecki:Seattle Genetics, Inc.: Research Funding. Abidi:Seattle Genetics, Inc.: Research Funding, Speakers Bureau. Chen:Seattle Genetics, Inc.: Research Funding. Stiff:Seattle Genetics, Inc.: Consultancy, Honoraria, Research Funding. Gianni:Seattle Genetics, Inc.: Research Funding. Carella:Seattle Genetics, Inc.: Research Funding. Osmanov:Seattle Genetics, Inc.: Research Funding. Bachanova:Seattle Genetics, Inc.: Consultancy, Research Funding. Sweetenham:Seattle Genetics, Inc.: Honoraria, Research Funding, Speakers Bureau. Sureda:Takeda Pharmaceuticals International Co.: Consultancy, Honoraria, Speakers Bureau; Seattle Genetics, Inc.: Research Funding. Huebner:Takeda Pharmaceuticals International Co.: Employment, Research Funding. Larsen:Seattle Genetics, Inc.: Employment, Equity Ownership. Hunder:Seattle Genetics, Inc.: Employment, Equity Ownership. Walewski:Seattle Genetics, Inc.: Research Funding; Takeda Poland: Consultancy, Travel expenses, Travel expenses Other.
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Jiang, Wei, Shen You, Jinyu Zhan, Xupeng Wang, Hong Lei i Deepak Adhikari. "Query-Efficient Generation of Adversarial Examples for Defensive DNNs via Multi-Objective Optimization". IEEE Transactions on Evolutionary Computation, 2022, 1. http://dx.doi.org/10.1109/tevc.2022.3231460.
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"Multi-Objective Big Data View Materialization using Improved Strength Pareto Evolutionary Algorithm". Journal of Information Technology Research 15, nr 1 (styczeń 2022): 0. http://dx.doi.org/10.4018/jitr.299947.
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Big data view materialization enhances the performance of Big data queries. This is a complex problem due to large volume, heterogeneity, high rate of data generation, low integrity and low value of Big data. Big data view materialization is a bi-objective optimization problem with the objectives - minimization of query evaluation time for a set of workload queries over a window of time and minimization of update processing cost of the views. Structure of Big data views can be represented as directed graph, which can be used to identify the candidate Big data views for a given set of queries. Evolutionary algorithms can be used to solve the problem of Big data view materialization. This paper presents an algorithm based on Strength Pareto Evolutionary Algorithm (SPEA-2) to generate a set of optimal solutions to the bi-objective Big data view selection problem.
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Nartey, Clement, Eric Tutu Tchao, James Dzisi Gadze, Bright Yeboah-Akowuah, Henry Nunoo-Mensah, Dominik Welte i Axel Sikora. "Blockchain-IoT peer device storage optimization using an advanced time-variant multi-objective particle swarm optimization algorithm". EURASIP Journal on Wireless Communications and Networking 2022, nr 1 (4.01.2022). http://dx.doi.org/10.1186/s13638-021-02074-3.
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AbstractThe integration of Internet of Things devices onto the Blockchain implies an increase in the transactions that occur on the Blockchain, thus increasing the storage requirements. A solution approach is to leverage cloud resources for storing blocks within the chain. The paper, therefore, proposes two solutions to this problem. The first being an improved hybrid architecture design which uses containerization to create a side chain on a fog node for the devices connected to it and an Advanced Time-variant Multi-objective Particle Swarm Optimization Algorithm (AT-MOPSO) for determining the optimal number of blocks that should be transferred to the cloud for storage. This algorithm uses time-variant weights for the velocity of the particle swarm optimization and the non-dominated sorting and mutation schemes from NSGA-III. The proposed algorithm was compared with results from the original MOPSO algorithm, the Strength Pareto Evolutionary Algorithm (SPEA-II), and the Pareto Envelope-based Selection Algorithm with region-based selection (PESA-II), and NSGA-III. The proposed AT-MOPSO showed better results than the aforementioned MOPSO algorithms in cloud storage cost and query probability optimization. Importantly, AT-MOPSO achieved 52% energy efficiency compared to NSGA-III. To show how this algorithm can be applied to a real-world Blockchain system, the BISS industrial Blockchain architecture was adapted and modified to show how the AT-MOPSO can be used with existing Blockchain systems and the benefits it provides.
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Zhao, Hang, Qinghua Deng, Wenting Huang, Dian Wang i Zhenping Feng. "Thermodynamic and Economic Analysis and Multi-objective Optimization of Supercritical CO2 Brayton Cycles". Journal of Engineering for Gas Turbines and Power 138, nr 8 (15.03.2016). http://dx.doi.org/10.1115/1.4032666.
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Supercritical CO2 Brayton cycles (SCO2BC) including the SCO2 single-recuperated Brayton cycle (RBC) and recompression recuperated Brayton cycle (RRBC) are considered, and flexible thermodynamic and economic modeling methodologies are presented. The influences of the key cycle parameters on thermodynamic performance of SCO2BC are studied, and the comparative analyses on RBC and RRBC are conducted. Nondominated Sorting Genetic Algorithm II (NSGA-II) is selected for the Pareto-based multi-objective optimization of the RRBC, with the maximum exergy efficiency and the lowest cost per power (k$/kW) as its objectives. Artificial neural network (ANN) is chosen to accelerate the parameters query process. It is shown that the cycle parameters such as heat source temperature, turbine inlet temperature, cycle pressure ratio, and pinch temperature difference of heat exchangers have significant effects on the cycle exergy efficiency. The exergy destruction of heat exchanger is the main reason why the exergy efficiency of RRBC is higher than that of the RBC under the same cycle conditions. RBC has a cost advantage from economic perspective, while RRBC has a much better thermodynamic performance, and could rectify the temperature pinching problem that exists in RBC. It is also shown that there is a conflicting relationship between the cycle cost/cycle power (CWR) and the cycle exergy efficiency. The optimization results could provide an optimum tradeoff curve enabling cycle designers to choose their desired combination between the efficiency and cost. ANN could help the users to find the SCO2BC parameters fast and accurately.
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Yang, Cai, Songhao Jia, Jizheng Yang i Haiyu Zhang. "Research on MapReduce heuristic multi table join algorithm based on binary optimization and pancake parallel strategy". Recent Patents on Engineering 17 (24.10.2022). http://dx.doi.org/10.2174/1872212117666221024161743.
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Background: With the development of technology, the data amount has increased significantly. In data processing, multi table query is the most frequently operation. Because the join keys cannot correspond one by one, there will be much redundant data transmission, resulting in a waste of network bandwidth. Objective: In order to solve the problems of network overhead and low efficiency, this paper proposes a heuristic multi table join optimization method. By sharing information, the unconnected tuples are eliminated, so as to reduce the amount of data transmitting. This shortens response time and improves the execution performance. Method: Firstly, the join key information of one table is compressed by the algorithm to make the filtered information for sharing. Then, the concurrent execution is controlled according to the pancake parallel strategy. Finally, the selection strategy of multi table join order is proposed. Results/Discussion: The experiments show that the proposed algorithm can filter a large amount of useless data and improve query efficiency. At the same time, the proposed algorithm reduces a lot of network overhead, improves the algorithm performance, and better solves the problem of low efficiency of multi table join. Conclusion: This paper introduces the heuristic strategy to optimize the algorithm, so that it can perform the join tasks in parallel, which further improves the performance of multi table join. The algorithm creatively combines heuristic data filtering, which greatly improves the quality of data processing. The algorithm is worth popularizing and applying.
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Cheng, Jian, Zhiji Zheng, Yinan Guo, Jiayang Pu i Shengxiang Yang. "Active broad learning with multi-objective evolution for data stream classification". Complex & Intelligent Systems, 12.08.2023. http://dx.doi.org/10.1007/s40747-023-01154-9.
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AbstractIn a streaming environment, the characteristics and labels of instances may change over time, forming concept drifts. Previous studies on data stream learning generally assume that the true label of each instance is available or easily obtained, which is impractical in many real-world applications due to expensive time and labor costs for labeling. To address the issue, an active broad learning based on multi-objective evolutionary optimization is presented to classify non-stationary data stream. The instance newly arrived at each time step is stored to a chunk in turn. Once the chunk is full, its data distribution is compared with previous ones by fast local drift detection to seek potential concept drift. Taking diversity of instances and their relevance to new concept into account, multi-objective evolutionary algorithm is introduced to find the most valuable candidate instances. Among them, representative ones are randomly selected to query their ground-truth labels, and then update broad learning model for drift adaption. More especially, the number of representative is determined by the stability of adjacent historical chunks. Experimental results for 7 synthetic and 5 real-world datasets show that the proposed method outperforms five state-of-the-art ones on classification accuracy and labeling cost due to drift regions accurately identified and the labeling budget adaptively adjusted.