Analyzing textual databases using data mining to enable fast product development processes

Currently, companies active in the design of high-tech products are confronted with a number of, often conflicting, challenges. Not only do they have to develop increasingly complex products in ever-shorter amounts of time but they also have to produce them cheaply for the local as well as global markets. In order to manage these conflicting trends adequately, as this paper will demonstrate, companies need to have high quality information at the right moment and at the right location. This serves as the motivation of this paper in which various databases within the product life cycle are examined. Due to the fact that unanticipated information plays a more and more dominant role, especially in highly innovative business processes, we focus our attention on textual databases since textual databases offer the best possibility to handle unanticipated free-formatted information. Also their treatment in the literature has thus far been scant. As will be demonstrated in this paper these databases have a huge potential for valuable information. Further, an analysis tool, data mining that could be used to analyse these textual databases so that we could extract information from them quickly and hence be able to access them at the right time when needed, is presented. We also highlight some of the difficulties faced when analyzing textual databases. Thus with the right information from the textual databases and the tools to deliver this information at the right time companies, would definitely be able to shorten development times and hence gain a competitive edge.     

挖掘RFID数据库中多维频繁路径的研究

针对应用于物流和供应链管理的射频识别(RFID)系统产生的海量路径数据集中的多维频繁路径挖掘的问题进行了深入的研究,提出了Dim-path与Path-dim两种不同的顺序处理非路径维数据和路径数据的多维频繁路径挖掘算法.这两种算法根据RFID路径数据自身的特点,将RFID数据划分为非路径维数据、位置数据、停留时间数据,...     

存储系统中的频繁访问模式挖掘

研究、分析了影响经典的模式挖掘方法挖掘频繁访问模式的效率,使其难以被存储系统接受的主要因素--噪音的产生原因和表现类型,提出一种具有噪音过滤能力,适应存储系统频繁访问序列模式挖掘的新方法--Z-Miner.Z-Miner使用全局分支裁剪和分支聚类方法来过滤噪音,对实际系统工作负载的模拟结果显示,Z-Miner指导的预取可以使缓存失效率降低40%～66%,平均响应时间降低26%～66%.相对经典挖掘方法,Z-Miner的挖掘开销有1～2个数量级的下降,而预取优化效果提高了1倍.     

一种快速的间接关联挖掘算法

给出了一个基于候选间接关联反单调性和频繁项目对支持矩阵的不需要生成所有频繁集的直接挖掘项目对之间间接关联的挖掘算法,并在一个Web log的真实数据集上进行了试验,与现有算法的比较表明该算法具有更好的性能。     

一种有效的数据流最大频繁模式挖掘算法

针对频繁项集挖掘存在数据和模式冗余的问题,对数据流最大频繁项集挖掘算法进行了研究。针对目前典型的数据流最大频繁模式挖掘算法DSM-MFI存在消耗大量存储空间及执行效率低等问题,提出了一种挖掘数据流界标窗口内最大频繁项集的算法MMFI-DS,该算法首先采用SEFI-tree存储包含在不断增长的数据流中相关最大频繁项集的重要信息,同时删除SEFI-tree中大量不频繁项目,然后使用自顶向下和自底向上双向搜索策略挖掘界标窗口中一系列的最大频繁项集。理论分析与实验表明,该算法比DSM-MFI算法具有更高的效率,并能节省存储空间。     

Data mining in e-commerce: A survey

Data mining has matured as a field of basic and applied research in computer science in general and e-commerce in particular. In this paper, we survey some of the recent approaches and architectures where data mining has been applied in the fields of e-commerce and e-business. Our intent is not to survey the plethora of algorithms in data mining; instead, our current focus being e-commerce, we limit our discussion to data mining in the context of e-commerce. We also mention a few directions for further work in this domain, based on the survey.     

Advanced clustering methods for mining chemical databases in forensic science

Heroin and cocaine gas chromatography data are analyzed using several clustering techniques. A database with clusters confirmed by police investigation is used to assess the potential of the analysis of the chemical signature of these drugs in the investigation process. Results are compared to standard methods in the field of chemical drug profiling and show that conventional approaches miss the inherent structure in the data, which is highlighted by methods such as spectral clustering and its variants. Also, an approach based on genetic programming is presented in order to tune the affinity matrix of the spectral clustering algorithm. Results indicate that all algorithms show a quite different behavior on the two datasets, but in both cases, the data exhibits a level of clustering, since there is at least one type of clustering algorithm that performs significantly better than chance. This confirms the relevancy of using chemical drugs databases in the process of understanding the illicit drugs market, as information regarding drug trafficking networks can likely be extracted from the chemical composition of drugs.     

基于周期采样的数据流频繁项集挖掘算法研究

针对用于数据流频繁项集挖掘的现有方法存在引入过多次频繁项集以及时空性能与输出精度较低的问题,利用Chebyshev不等式,构造了项集频度周期采样的概率误差边界,给出了动态检测项集支持度变化方法.提出了一种基于周期采样的数据流频繁项集挖掘算法FI-PS,该算法通过跟踪项集支持度变化确定项集支持度的稳定性,并以此作为调整窗口大小以及采样周期的依据,从而以一个较大的概率保证项集支持度误差有上界.理论分析及实验证明该算法有效,在保证挖掘结果准确度相对较好的条件下,可获得较优执行性能.     

Co-authorship networks and research impact in large research facilities: benchmarking internal reports and bibliometric databases

Scientometrics - In this paper, we address the main differences of the scientific production between internal and external researcher groups of a synchrotron radiation facility. Through the...     

Interestingness of association rules in data mining: Issues relevant to e-commerce

The ubiquitous low-cost connectivity synonymous with the internet has changed the competitive business environment by dissolving traditional sources of competitive advantage based on size, location and the like. In this level playing field, firms are forced to compete on the basis of knowledge. Data mining tools and techniques provide e-commerce applications with novel and significant knowledge. This knowledge can be leveraged to gain competitive advantage. However, the automated nature of data mining algorithms may result in a glut of patterns — the sheer numbers of which contribute to incomprehensibility. Importance of automated methods that address this immensity problem, particularly with respect to practical application of data mining results, cannot be overstated. We first examine different approaches to address this problem citing their applicability to e-commerce whenever appropriate. We then provide a detailed survey of one important approach, namely interestingness measure, and discuss its relevance in e-commerce applications such as personalization in recommender systems. Study of current literature brings out important issues that reveal many promising avenues for future research. We conclude by reiterating the importance of post-processing methods in data mining for effective and efficient deployment of e-commerce solutions.     

RFID-based multi-attribute logistics information processing and anomaly mining in production logistics

Timely collecting logistics information and finding anomalies of material supply plays a critical role in modern manufacturing systems. The problem is how to obtain multi-attribute logistics information of production logistics and build an effective approach for mining anomalies from the huge number of RFID data. The multi-attribute, randomness and various measure units of logistics states further aggravate the problem. In this paper, a novel RFID-based logistics information processing approach is proposed. Firstly, the state features of production logistics is discussed from multi-attribute perspectives including time, location, quantities, sequence and path, and a set of calculating models is set up to process RFID data for getting multi-attribute state data. Furthermore, in case of the randomness and various measure units of state data, a similarity model is presented to unify measure units of state data, and a clustering approach is proposed to divide the huge number of RFID data into different clusters with high close degree for finding out anomalies. Lastly, the experimental results show that the proposed approach can efficiently find out more than 90% of anomalies among production logistics.     

Text mining applied to patent mapping: a practical business case

Professional patent searchers are traditionally rather suspicious of the alleged “black box” effect inherently attached to intelligent software engines relying upon linguistic technologies for patent analysis and mapping. In this article, the authors propose that such prejudices can be overcome by setting a realistic business objective while experimenting with these new linguistic tools, as well as by applying serious methodology for validating the results of the analysis. The strengths and weaknesses of a particular text mining tool are assessed with reference to a practical business case in the field of packaging technology, and a comparison of the outcome of such an analysis with a traditional one, carried out using conventional patent classifications, is also described.     

Bridging customer knowledge to innovative product development: a data mining approach

In the big data era, firms are inundated with customer data, which are valuable in improving services, developing new products, and identifying new markets. However, it is not clear how companies apply data-driven methods to facilitate customer knowledge management when developing innovative new products. Studies have investigated the specific benefits of applying data-driven methods in customer knowledge management, but failed to systematically investigate the specific mechanics of how firms realised these benefits. Accordingly, this study proposes a systematic approach to link customer knowledge with innovative product development in a data-driven environment. To mine customer needs, this study adopts the Apriori algorithm and C5.0 in addition to the association rule and decision tree methodologies for data mining. It provides a systematic and effective method for managers to extract knowledge ‘from’ and ‘about’ customers to identify their preferences, enabling firms to develop the right products and gain competitive advantages. The findings indicate that the knowledge-based approach is effective, and the knowledge extracted is shown as a set of rules that can be used to identify useful patterns for both innovative product development and marketing strategies.     

分集药包在爆破封堵非法盗采煤窑中的应用

为彻底封堵非法盗采煤窑,采用分集药包爆破的方法,来破坏非法盗采煤窑的内部结构。盗采煤窑深度30～40 m不等,依据类似工程经验,每个煤窑需要炸毁的长度约为20 m;采用分集装药结构,将装药的药包均匀分布于巷道内,每个药包间距4 m,参考硐室爆破计算,获得分集药包位置和爆破参数。为了保证爆破安全及封堵效果,在硐内部支巷用编织袋装土后进行填塞,填塞长度不少于8m。爆后检查结果为煤窑顶板围岩坍塌,沿巷道形成爆破漏斗,达到封堵目的。     

Fast and facile preparation of superhigh aspect-ratio Cu-thiourea nanowires in large quantity

Superhigh aspect-ratio Cu-thiourea (Cu(tu)) nanowires have been synthesized in large quantity via a fast and facile method. Nanowires of Cu(tu)Cl·0.5H₂O and Cu(tu)Br·0.5H₂O were found to be 60-100 nm and 100-200 nm in diameter, and could extend to several millimeters in length. It is found to be the most convenient and facile approach to the fabrication of one-dimensional superhigh aspect-ratio nanomaterials in large scale so far.     

The viability of remote mining communities: insights from community perceptions and employment impact assessments

This article examines community perceptions and employment impacts to assess ex ante the post-mining viability of two settlements in remote north-east South Australia. The results reinforce grave concerns over their future viability following mine closure. However, government employees and traditional owners are more optimistic than other groups about community futures beyond mining. Devastatingly, employment impacts represent approximately 80 and 75% (225 & 1616) of jobs and 90 and 80% ($30 m & $164 m) of employment income in the two locations (Leigh Creek, Roxby Downs). These impacts reach beyond mining into other sectors, particularly Public Services and Trade and Accommodation respectively, and further limit economic diversity and opportunity. Building alternative futures in remote locations like these is problematic, though not impossible. Policy responses should reflect differences in perceptions, impact and case-specific contexts: settlements at the end of their life cycles like Leigh Creek require emergency action including institutional reform; settlements still in the early stages like Roxby Downs have time to begin planning now, though not yet conceived, for a future without mining.     

Data mining and accelerated electronic structure theory as a tool in the search for new functional materials

A highly accelerated electronic structure implementation and data mining algorithms have been combined with structural data from the inorganic crystal structure database to generate materials properties for about 22,000 inorganic compounds. It is shown how data mining algorithms employed on the database can identify new functional materials with desired materials properties, resulting in a prediction of 136 novel materials with potential for use as detector materials for ionizing radiation. The methodology behind the automatized ab initio approach is presented, results are tabulated and a version of the complete database is made available at the internet web site <http://gurka.fysik.uu.se/ESP/> (Ref. [1]).     

纳滤膜分离技术在矿井水处理中的研究

介绍了用纳滤膜处理含悬浮物矿井水的工艺流程,考察了纳滤膜不同操作特性对膜过滤性能的影响及组合工艺对矿井水的处理效果.试验结果表明:纳滤膜在过滤周期为30min采用逆料液回流冲洗,化学清洗周期5天,能够有效减少膜污染,恢复膜通量,稳定运行4周的试验数据表明,该工艺对矿井水的处理效果明显,出水稳定,出水达到国家饮用水标准,是一种高效、经济的处理工艺.     

基于FCM与粗糙集的连续数据知识挖掘方法

对连续数据知识挖掘可采用模糊C均值聚类将其离散化，再应用粗糙集进行约简．文中针对该过程中样本数据离散化后出现多个聚类中心隶属度值较接近的情况．提出了隶属度重叠度的方法来扩展对应离散类别的选取．离散类剐确定后再应用粗糙集理论对其进行知识挖掘，可得到连续数据的本质特性．此方法应用于汽轮机轴系振动数据的知识挖掘．与采用最大隶属度确定对应类别的方法相比，该方法不影响对连续数据的知识挖掘，并能真实反映连续数据的特性．     

The use of fracture mechanics in failure analysis in the offshore diamond mining industry

This paper considers two major failures of offshore diamond mining equipment, which should not have occurred had both fracture mechanics aspects and materials behaviour been more fully understood. The two case studies include (a) failure of a wire rope swivel, and (b) failure of a load cell (used for monitoring rope load). In case (a) the swivel shank had failed in reverse bending fatigue, although it was thought initially to experience only tensile stresses. The linkage system to the haul rope, through a form of clevis plates, led to high bending fatigue stresses, and the failure was exacerbated by inappropriate remedial changes, including poor choice of material, as well as stress concentrating effects induced by a shrink fit stainless steel collar. In case (b), complete collapse of a gantry, sheave wheel system and partial A frame support resulted from the prior brittle failure of the load cell, and this could be attributed to incorrect heat treatment of the load cell material. In both cases quantitative use is made of fracture mechanics in the failure assessment.