Scene invariant crowd counting and crowd occupancy analysis

In public places, crowd size may be an indicator of congestion, delay, instability, or of abnormal events, such as a fight, riot or emergency. Crowd related information can also provide important business intelligence such as the distribution of people throughout spaces, throughput rates, and local densities. A major drawback of many crowd counting approaches is their reliance on large numbers of holistic features, training data requirements of hundreds or thousands of frames per camera, and that each camera must be trained separately. This makes deployment in large multi-camera environments such as shopping centres very costly and difficult. In this chapter, we present a novel scene-invariant crowd counting algorithm that uses local features to monitor crowd size. The use of local features allows the proposed algorithm to calculate local occupancy statistics, scale to conditions which are unseen in the training data, and be trained on significantly less data. Scene invariance is achieved through the use of camera calibration, allowing the system to be trained on one or more viewpoints and then deployed on any number of new cameras for testing without further training. A pre-trained system could then be used as a ‘turn-key’ solution for crowd counting across a wide range of environments, eliminating many of the costly barriers to deployment which currently exist.

Identifying customer behaviour and dwell time using soft biometrics

In a commercial environment, it is advantageous to know how long it takes customers to move between different regions, how long they spend in each region, and where they are likely to go as they move from one location to another. Presently, these measures can only be determined manually, or through the use of hardware tags (i.e. RFID). Soft biometrics are characteristics that can be used to describe, but not uniquely identify an individual. They include traits such as height, weight, gender, hair, skin and clothing colour. Unlike traditional biometrics, soft biometrics can be acquired by surveillance cameras at range without any user cooperation. While these traits cannot provide robust authentication, they can be used to provide identification at long range, and aid in object tracking and detection in disjoint camera networks. In this chapter we propose using colour, height and luggage soft biometrics to determine operational statistics relating to how people move through a space. A novel average soft biometric is used to locate people who look distinct, and these people are then detected at various locations within a disjoint camera network to gradually obtain operational statistics

Leveraging social network data for analytical CRM strategies : the introduction of social BI

The skyrocketing trend for social media on the Internet greatly alters analytical Customer Relationship Management (CRM). Against this backdrop, the purpose of this paper is to advance the conceptual design of Business Intelligence (BI) systems with data identified from social networks. We develop an integrated social network data model, based on an in-depth analysis of Facebook. The data model can inform the design of data warehouses in order to offer new opportunities for CRM analyses, leading to a more consistent and richer picture of customers? characteristics, needs, wants, and demands. Four major contributions are offered. First, Social CRM and Social BI are introduced as emerging fields of research. Second, we develop a conceptual data model to identify and systematize the data available on online social networks. Third, based on the identified data, we design a multidimensional data model as an early contribution to the conceptual design of Social BI systems and demonstrate its application by developing management reports in a retail scenario. Fourth, intellectual challenges for advancing Social CRM and Social BI are discussed.

Enterprise systems curricula for Asia-Pacific region : a vision for 2020

Enterprise Systems (ES) have emerged as possibly the most important and challenging development in the corporate use of information technology in the last decade. Organizations have invested heavily in these large, integrated application software suites expecting improvments in; business processes, management of expenditure, customer service, and more generally, competitiveness, improved access to better information/knowledge (i.e., business intelligence and analytics). Forrester survey data consistently shows that investment in ES and enterprise applications in general remains the top IT spending priority, with the ES market estimated at $38 billion and predicted to grow at a steady rate of 6.9%, reaching $50 billion by 2012 (Wang & Hamerman, 2008). Yet, organizations have failed to realize all the anticipated benefits. One of the key reasons is the inability of employees to properly utilize the capabilities of the enterprise systems to complete the work and extract information critical to decision making. In response, universities (tertiary institutes) have developed academic programs aimed at addressing the skill gaps. In parallel with the proliferation of ES, there has been growing recognition of the importance of Teaching Enterprise Systems at tertiary education institutes. Many academic papers have discused the important role of Enterprise System curricula at tertiary education institutes (Ask, 2008; Hawking, 2004; Stewart, 2001), where the teaching philosophises, teaching approaches and challenges in Enterprise Systems education were discussed. Following the global trends, tertiary institutes in the Pacific-Asian region commenced introducing Enterprise System curricula in late 1990s with a range of subjects (a subject represents a single unit, rather than a collection of units; which we refer to as a course) in faculties / schools / departments of Information Technology, Business and in some cases in Engineering. Many tertiary educations commenced their initial subject offers around four salient concepts of Enterprise Systems: (1) Enterprise Systems implementations, (2) Introductions to core modules of Enterprise Systems, (3) Application customization using a programming language (e.g. ABAP) and (4) Systems Administration. While universities have come a long way in developing curricula in the enterprise system area, many obstacles remain: high cost of technology, qualified faculty to teach, lack of teaching materials, etc.

Learning domain-specific sentiment lexicons for predicting product sales

Generic sentiment lexicons have been widely used for sentiment analysis these days. However, manually constructing sentiment lexicons is very time-consuming and it may not be feasible for certain application domains where annotation expertise is not available. One contribution of this paper is the development of a statistical learning based computational method for the automatic construction of domain-specific sentiment lexicons to enhance cross-domain sentiment analysis. Our initial experiments show that the proposed methodology can automatically generate domain-specific sentiment lexicons which contribute to improve the effectiveness of opinion retrieval at the document level. Another contribution of our work is that we show the feasibility of applying the sentiment metric derived based on the automatically constructed sentiment lexicons to predict product sales of certain product categories. Our research contributes to the development of more effective sentiment analysis system to extract business intelligence from numerous opinionated expressions posted to the Web

Augmenting analytical CRM strategies with social BI

Large communities built around social media on the Internet offer an opportunity to augment analytical customer relationship management (CRM) strategies. The purpose of this paper is to provide direction to advance the conceptual design of business intelligence (BI) systems for implementing CRM strategies. After introducing social CRM and social BI as emerging fields of research, the authors match CRM strategies with a re-engineered conceptual data model of Facebook in order to illustrate the strategic value of these data. Subsequently, the authors design a multi-dimensional data model for social BI and demonstrate its applicability by designing management reports in a retail scenario. Building on the service blueprinting framework, the authors propose a structured research agenda for the emerging field of social BI.

A data analytics case study assessing factors affecting pavement deflection values

Road networks are a national critical infrastructure. The road assets need to be monitored and maintained efficiently as their conditions deteriorate over time. The condition of one of such assets, road pavement, plays a major role in the road network maintenance programmes. Pavement conditions depend upon many factors such as pavement types, traffic and environmental conditions. This paper presents a data analytics case study for assessing the factors affecting the pavement deflection values measured by the traffic speed deflectometer (TSD) device. The analytics process includes acquisition and integration of data from multiple sources, data pre-processing, mining useful information from them and utilising data mining outputs for knowledge deployment. Data mining techniques are able to show how TSD outputs vary in different roads, traffic and environmental conditions. The generated data mining models map the TSD outputs to some classes and define correction factors for each class.

Organizational competencies and dynamic Accounting Information System capability: Impact on AIS processes and firm performance

Accounting information systems (AIS) capture and process accounting data and provide valuable information for decision-makers. However, in a rapidly changing environment, continual management of the AIS is necessary for organizations to optimise performance outcomes. We suggest that building a dynamic AIS capability enables accounting process and organizational performance. Using the dynamic capabilities framework (Teece 2007) we propose that a dynamic AIS capability can be developed through the synergy of three competencies: a flexible AIS, having a complementary business intelligence system and accounting professionals with IT technical competency. Using survey data, we find evidence of a positive association between a dynamic AIS capability, accounting process performance, and overall firm performance. The results suggest that developing a dynamic AIS resource can add value to an organization. This study provides guidance for organizations looking to leverage the performance outcomes of their AIS environment.

Combining heterogeneous classifiers for relational databases

Practical usage of machine learning is gaining strategic importance in enterprises looking for business intelligence. However, most enterprise data is distributed in multiple relational databases with expert-designed schema. Using traditional single-table machine learning techniques over such data not only incur a computational penalty for converting to a flat form (mega-join), even the human-specified semantic information present in the relations is lost. In this paper, we present a practical, two-phase hierarchical meta-classification algorithm for relational databases with a semantic divide and conquer approach. We propose a recursive, prediction aggregation technique over heterogeneous classifiers applied on individual database tables. The proposed algorithm was evaluated on three diverse datasets. namely TPCH, PKDD and UCI benchmarks and showed considerable reduction in classification time without any loss of prediction accuracy. (C) 2012 Elsevier Ltd. All rights reserved.

Charles M. Breder, Jr.: Palmetto Key, 1942

Charles M. Breder and his wife Ethel spent part of the summer of 1942 at the Palmetto Key field station, known today as Cabbage Key, on the west coast of Florida south of Charlotte Harbor. The Palmetto Key field station began in 1938 and ended in 1942 because of World War II. His Palmetto Key diary ran for 95 pages of notes, tables, diagrams, drawings, lists, and business records and this report presents a variety of fascinating entries. Diaries from other years all bear Breder's style of discipline, curiosity, humor, and speculations on nature. The diary was transcribed as part of the Coastal Estuarine Data/Document Rescue and Archeology effort for South Florida. (PDF contaons 24 pages)

商务智能中数据分析代理(DAA)模式的研究与实践

在介绍了数据分析代理的概念后,提出了数据分析代理模式的体系结构,讨论了在不同类型企业中数据分析代理的具体应用模式企业内代理模式和企业外代理模式,对比分析了数据分析传统模式和代理模式二者之间特点,最后举例说明了数据分析代理模式在企业中的具体实践。

商业智能在银联业务分析系统中的应用研究

随着国内金融行业的逐步开放,中国银联也面临着跨国银行卡组织的激烈竞争，跨国银行卡组织拥有先进的IT技术和经营管理经验，和中国银联相比，具有很大竞争优势。银联为了积极面对跨国银行卡组织的挑战，成为一个知名品牌，必须加快信息化建设，转变经营观念，从传统的以业务为中心转移到以客户为中心，而商业智能技术——数据仓库和数据挖掘正是银联信息化建设的重要保障。 本文首先分析了银联的实际业务需求，结合银联的具体业务特点，设计与实现了银联的数据仓库系统，着重对数据仓库技术在银联中的应用现状进行了详细表述；该系统采用总线式的设计架构，有很好的一致性和可扩展性；系统采用维度建模方法进行数据仓库的逻辑设计，维度建模方法能很好地提高系统查询性能，在逻辑设计基础上本文也进行了数据仓库的物理设计。同时本文也详细介绍了数据仓库的重点部分——ETL系统的设计和实现，该ETL系统采用模块化的设计方法，采用元数据作为驱动方式，加强了调度管理和监控的功能，使该ETL工具更具智能性和更好的适应性。 本文在完成银联数据仓库系统建设的基础上，详细分析了银联业务系统要实现的OLAP分析目标，介绍了数据挖掘技术在银联客户分类中的应用，首次尝试在银联数据仓库系统中构建客户分类模型。在客户分类模型的构建中，我们首先采用聚类技术进行客户群分类，然后使用改进的SLIQ算法构建分类模型，本文对SLIQ算法中的符号型属性处理方法及其剪枝算法进行改进，并对结果进行对比分析，得到了一个较为合理的客户分类模型，取得了很好的应用效果，从而为银联数据仓库系统开发应用提供了可借鉴的操作思路。