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道德概念是一种HPC概念吗?——浅议厚道德概念与道德概念自然化

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北京科技大学学报（社会科学版） 2025年第1期41卷 129-136页

作者：王奕文中国人民大学哲学院北京100872

道德概念与自然概念的关系是元伦理学领域的重要议题,自然主义者同意道德概念可以通过自然概念说明却被摩尔批判为“自然主义谬误”,博伊德提出属性稳态丛聚理论(homeostatic property cluster,以下简称HPC)以规避该批评。定义HPC类词... 详细信息

道德概念与自然概念的关系是元伦理学领域的重要议题,自然主义者同意道德概念可以通过自然概念说明却被摩尔批判为“自然主义谬误”,博伊德提出属性稳态丛聚理论(homeostatic property cluster,以下简称HPC)以规避该批评。定义HPC类词项的内在属性间应具有因果联系,但对“善”概念进行的两个思想实验证明“善”概念不具有归纳推理适宜性,这意味着薄道德概念难以具备成为HPC概念的必要条件。厚道德概念由于含有丰富的描述性内容更有利于归纳。文章试将厚道德概念作为HPC概念定义并确定其丛聚属性,以维护部分道德概念的自然性、彰显道德概念的历史与社会维度。

关键词： HPC概念厚概念道德自然主义实在论

MiNT-OLAP cluster： minimizing network transmission cost in OLAP cluster for main memory analytical database

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Frontiers of Computer Science 2012年第6期6卷 668-676页

作者： Min JIAO Yansong ZHANG Zhanwei WANG Shan WANG DEKE Lab Renmin University of ChinaBeijing 100872China National Survey Research Center Renmin University of ChinaBeijing 100872China School of Information Renmin University of ChinaBeijing 100872China

Powerful storage, high performance and scalability are the most important issues for analytical databases. These three factors interact with each other, for example, powerful storage needs less scalability but higher performance, high performance means less consumption of indexes and other materializations for storage and fewer processing nodes, larger scale relieves stress on powerful storage and the high performance processing engine. Some analytical databases （ParAccel, Teradata） bind their performance with advanced hardware supports, some （Asterdata, Greenplum） rely on the high scalability framework of MapReduce, some （MonetDB, Sybase IQ, Vertica） highlight performance on processing engine and storage engine. All these approaches can be integrated into an storage-performance-scalability （S- P-S） model, and future large scale analytical processing can be built on moderate clusters to minimize expensive hardware dependency. The most important thing is a simple software framework is fundamental to maintain pace with the development of hardware technologies. In this paper, we propose a schemaaware on-line analytical processing （OLAP） model with deep optimization from native features of the star or snowflake schema. The OLAP model divides the whole process into several stages, each stage pipes its output to the next stage, we minimize the size of output data in each stage, whether in central processing or clustered processing. We extend this mechanism to cluster processing using two major techniques, one is using NetMemory as a broadcasting protocol based dimension mirror synchronizing buffer, the other is predicatevector based DDTA-OLAP cluster model which can minimize the data dependency of star-join using bitmap vectors. Our OLAP model aims to minimize network transmission cost （MINT in short） for OLAP clusters and support a scalable but simple distributed storage model for large scale clustering processing. Finally, the experimental results show the speedup and s

关键词： OLAP cluster MINT NetMemory schemaaware OLAP

Timestamp reassignment:taming transaction abort for serializable snapshot isolation

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Frontiers of Computer Science 2019年第6期13卷 1282-1295页

作者： Ningnan ZHOU Xiao ZHANG Shan WANG MOE Key Laboratory of DEKE Renmin University of ChinaBeijing 100872China School of Information Renmin University of ChinaBeijing 100872China

Serializable snapshot isolation(SSI)is a promising technique to exploit parallelism for multi-core ***,SSI suffers from excessive transaction *** remedies have three drawbacks:1)tracking prohibitively transitive dependencies;2)aborting on every writewrite conflict;and 3)requiring manual annotation on transaction *** this paper,we propose to suppress transaction aborts by reassigning *** combine static analysis with augmented query *** this way,we save both aborts caused by read-write and write-write conflicts,without tracking transitive dependency and annotating transaction *** such,our approach does not exhibit drawbacks of existing *** experiments demonstrate the effectiveness and practicality of our approach.

关键词： serializable snapshot isolation timestamp reassignment static analysis augmented query plan

MRST-- An Efficient Monitoring Technology of Summarization on Stream Data

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Journal of Computer Science & Technology 2007年第2期22卷 190-196页

作者：樊小泊解婷婷李翠平陈红 School of Information Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing 100872 China

Monitoring on data streams is an efficient method of acquiring the characters of data stream. However the available resources for each data stream are limited, so the problem of how to use the limited resources to process infinite data stream is an open challenging problem. In this paper, we adopt the wavelet and sliding window methods to design a multi-resolution summarization data structure, the Multi-Resolution Summarization Tree （MRST） which can be updated incrementally with the incoming data and can support point queries, range queries, multi-point queries and keep the precision of queries. We use both synthetic data and real-world data to evaluate our algorithm. The results of experiment indicate that the efficiency of query and the adaptability of MRST have exceeded the current algorithm, at the same time the realization of it is simpler than others.

关键词： Haar wavelet sliding window stream data

马克思“机器论片段”语境下的数字技术再反思

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海南大学学报（人文社会科学版） 2025年第1期43卷 60-67页

作者：王文陶厚兴中国人民大学重阳金融研究院北京100872 兰州大学马克思主义学院甘肃兰州730000

马克思“机器论片段”重点论述了机器体系的产生及其在社会生产中的应用所引起的劳动资料技术化、一般智力对象化、固定资本扩大化、人机关系异化等一系列深刻变革。数字技术具有物质技术与社会历史双重属性,其一方面作为人造的“物体系... 详细信息

马克思“机器论片段”重点论述了机器体系的产生及其在社会生产中的应用所引起的劳动资料技术化、一般智力对象化、固定资本扩大化、人机关系异化等一系列深刻变革。数字技术具有物质技术与社会历史双重属性,其一方面作为人造的“物体系”,克服与弥补人类自身劳动力的不足,另一方面作为生产关系与资本权力的物性表达。数字技术的广泛应用导致工业时代的“机器悖论”演化为数字时代的“智能悖论”,集中表现为“劳动悖论”“时间悖论”与“解放悖论”。马克思“机器论片段”的机器技术思想对我们在数字时代审思数字技术,探索扬弃技术的权力与宰制,消解人与技术的异化关系,使技术真正摆脱压迫、剥削人的工具地位,成为提高劳动生产率,缩短必要劳动时间和增加自由支配时间,促进人类劳动解放与自由全面发展的现实路径依然具有理论与实践价值。

关键词：马克思机器论片段一般智力固定资本数字技术

Identification of Linear Systems Using Binary Sensors with Random Thresholds

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Journal of Systems Science & Complexity 2024年第3期37卷 907-923页

作者： HUANG Zhiyong SONG Qijiang School of Mathematics Renmin University of ChinaBeijing 100872China School of Mathematics Sino-French InstituteRenmin University of ChinaBeijing 100872China

In this paper,the problem of identifying autoregressive-moving-average systems under random threshold binary-valued output measurements is *** the help of stochastic approximation algorithms with expanding truncations,the authors give the recursive estimates for the parameters of both the linear system and the binary *** reasonable conditions,all constructed estimates are proved to be convergent to the true values with probability one,and the convergence rates are also established.A simulation example is provided to justify the theoretical results.

关键词： α-mixing binary sensor quantized system stochastic approximation stochastic thresh-old strongly consistent system identification

Indexing Future Trajectories of Moving Objects in a Constrained Network

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Journal of Computer Science & Technology 2007年第2期22卷 245-251页

作者：陈继东孟小峰 School of Information Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing 100872 China

Advances in wireless sensor networks and positioning technologies enable new applications monitoring moving objects. Some of these applications, such as traffic management, require the possibility to query the future trajectories of the objects. In this paper, we propose an original data access method, the ANR-tree, which supports predictive queries. We focus on real life environments, where the objects move within constrained networks, such as vehicles on roads. We introduce a simulation-based prediction model based on graphs of cellular automata, which makes full use of the network constraints and the stochastic traffic behavior. Our technique differs strongly from the linear prediction model, which has low prediction accuracy and requires frequent updates when applied to real traffic with velocity changing frequently. The data structure extends the R-tree with adaptive units which group neighbor objects moving in the similar moving patterns. The predicted movement of the adaptive unit is not given by a single trajectory, but instead by two trajectory bounds based on different assumptions on the traffic conditions and obtained from the simulation. Our experiments, carried on two different datasets, show that the ANR-tree is essentially one order of magnitude more efficient than the TPR-tree, and is much more scalable.

关键词： database spatial database access methods moving objects

Search Result Diversification Based on Query Facets

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Journal of Computer Science & Technology 2015年第4期30卷 888-901页

作者：胡莎窦志成王晓捷文继荣 School of Information Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing 100872 China

In search engines, different users may search for different information by issuing the same query. To satisfy more users with limited search results, search result diversification re-ranks the results to cover as many user intents as possible. Most existing intent-aware diversification algorithms recognize user intents as subtopics, each of which is usually a word, a phrase, or a piece of description. In this paper, we leverage query facets to understand user intents in diversification, where each facet contains a group of words or phrases that explain an underlying intent of a query. We generate subtopics based on query facets and propose faceted diversification approaches. Experimental results on the public TREC 2009 dataset show that our faceted approaches outperform state-of-the-art diversification models.

关键词： query intent query facet search result diversification

Improving performance by creating a native join-index for OLAP

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Frontiers of Computer Science 2011年第2期5卷 236-249页

作者： Yansong ZHANG Shan WANG Jiaheng LU National Survey Research Center at Renmin University of China Beijing 100872 China Key Laboratory of the Ministry of Education for Data Engineering and Knowledge Engineering Renmin University of China Beijing 100872 China School of Information Renmin University of China Beijing 100872 China

The performance of online analytical processing （OLAP） is critical for meeting the increasing requirements of massive volume analytical applications. Typical techniques, such as in-memory processing, column-storage, and join indexes focus on high perfor- mance storage media, efficient storage models, and reduced query processing. While they effectively perform OLAP applications, there is a vital limitation： main- memory database based OLAP （MMOLAP） cannot provide high performance for a large size data set. In this paper, we propose a novel memory dimension table model, in which the primary keys of the dimension table can be directly mapped to dimensional tuple addresses. To achieve higher performance of dimensional tuple access, we optimize our storage model for dimension tables based on OLAP query workload features. We present directly dimensional tuple accessing （DDTA） based join （DDTA- JOIN）, a technique to optimize query processing on the memory dimension table by direct dimensional tuple access. We also contribute by proposing an optimization of the predicate tree to shorten predicate operation length by pruning useless predicate processing. Our experimental results show that the DDTA-JOIN algorithm is superior to both simulated row-store main memory query processing and the open-source column-store main memory database MonetDB, thanks to the reduced join cost and simple yet efficient query processing.

关键词： directly dimensional tuple accessing (DDTA) – DDTA JOIN – native join index – predicate tree