Semantic data mining and linked data for a recommender system in the AEC industry

Even though it can provide design teams with valuable performance insights and enhance decision-making, monitored building data is rarely reused in an effective feedback loop from operation to design. Data mining allows users to obtain such insights from the large datasets generated throughout the building life cycle. Furthermore, semantic web technologies allow to formally represent the built environment and retrieve knowledge in response to domain-specific requirements. Both approaches have independently established themselves as powerful aids in decision-making. Combining them can enrich data mining processes with domain knowledge and facilitate knowledge discovery, representation and reuse. In this article, we look into the available data mining techniques and investigate to what extent they can be fused with semantic web technologies to provide recommendations to the end user in performance-oriented design. We demonstrate an initial implementation of a linked data-based system for generation of recommendations

Resist Adversary in Modified Net Explore

In this paper, user profiles, portrayals of user supplies, can be absorbed via search engine for to give customized look for results. Rich techniques capture user for building user information through proxies web servers (to catch scanning histories).These jointly need servicing of the user to provide the proxies server. In this reading, we examine the consumption of a less-invasive means modifying to unclear concerns has extended been an important aspect in the analysis of Data Recovery. Personalized look for has as of late got amazing regard for location this analyze in the web search set, in light of the begin that a user’s general sensation might help the search engine for disambiguate the legitimate plan of an query. The customized look for has been suggested for some a long time and many customization methods have been researched, it is still unclear whether customization is effectively practical on different questions for unique users, and under unique search configurations. In this paper, we focus on how to infer a user’s attention from the user’s search connection and usage the deduced certain user design for customized search. We analyzed defense insurance in PWS applications that design user tendency as modern user information. This system suggested a PWS framework called UPS that can adaptively sum up information by reviews although regarding user mentioned protection requirements. We confirmed two greedy computations, in certain GreedyDP what’s more GreedyIL, for runtime rumors. We will avoid opponents with wider history knowledge, such as richer connection among subjects or capability to catch a series of queries from the victim. We will also search for more innovative technique to build the user information, and better analytics to estimate the efficiency of UPS. DOI: 10.17762/ijritcc2321-8169.15071

User-centred interface design for cross-language information retrieval

This paper reports on the user-centered design methodology and techniques used for the elicitation of user requirements and how these requirements informed the first phase of the user interface design for a Cross-Language Information Retrieval System. We describe a set of factors involved in analysis of the data collected and, finally discuss the implications for user interface design based on the findings

A Generic Alerting Service for Digital Libraries

Users of modern digital libraries (DLs) can keep themselves up-to-date by searching and browsing their favorite collections, or more conveniently by resorting to an alerting service. The alerting service notifies its clients about new or changed documents. Proprietary and mediating alerting services fail to fluidly integrate information from differing collections. This paper analyses the conceptual requirements of this much-sought after service for digital libraries. We demonstrate that the differing concepts of digital libraries and its underlying technical design has extensive influence (a) the expectations, needs and interests of users regarding an alerting service, and (b) on the technical possibilities of the implementation of the service. Our findings will show that the range of issues surrounding alerting services for digital libraries, their design and use is greater than one may anticipate. We also show that, conversely, the requirements for an alerting service have considerable impact on the concepts of DL design. Our findings should be of interest for librarians as well as system designers. We highlight and discuss the far-reaching implications for the design of, and interaction with, libraries. This paper discusses the lessons learned from building such a distributed alerting service. We present our prototype implementation as a proof-of-concept for an alerting service for open DL software

Approximative filtering of XML documents in a publish/subscribe system

Publish/subscribe systems filter published documents and inform their subscribers about documents matching their interests. Recent systems have focussed on documents or messages sent in XML format. Subscribers have to be familiar with the underlying XML format to create meaningful subscriptions. A service might support several providers with slightly differing formats, e.g., several publishers of books. This makes the definition of a successful subscription almost impossible. This paper proposes the use of an approximative language for subscriptions. We introduce the design of our ApproXFilter algorithm for approximative filtering in a publish/subscribe system. We present the results of our performance analysis of a prototypical implementation

An active, ontology-driven network service for Internet collaboration

Web portals have emerged as an important means of collaboration on the WWW, and the integration of ontologies promises to make them more accurate in how they serve users’ collaboration and information location requirements. However, web portals are essentially a centralised architecture resulting in difficulties supporting seamless roaming between portals and collaboration between groups supported on different portals. This paper proposes an alternative approach to collaboration over the web using ontologies that is de-centralised and exploits content-based networking. We argue that this approach promises a user-centric, timely, secure and location-independent mechanism, which is potentially more scaleable and universal than existing centralised portals

The state-of-the-art in personalized recommender systems for social networking

With the explosion of Web 2.0 application such as blogs, social and professional networks, and various other types of social media, the rich online information and various new sources of knowledge flood users and hence pose a great challenge in terms of information overload. It is critical to use intelligent agent software systems to assist users in finding the right information from an abundance of Web data. Recommender systems can help users deal with information overload problem efficiently by suggesting items (e.g., information and products) that match users’ personal interests. The recommender technology has been successfully employed in many applications such as recommending films, music, books, etc. The purpose of this report is to give an overview of existing technologies for building personalized recommender systems in social networking environment, to propose a research direction for addressing user profiling and cold start problems by exploiting user-generated content newly available in Web 2.0

Building a domain-specific document collection for evaluating metadata effects on information retrieval

This paper describes the development of a structured document collection containing user-generated text and numerical metadata for exploring the exploitation of metadata in information retrieval (IR). The collection consists of more than 61,000 documents extracted from YouTube video pages on basketball in general and NBA (National Basketball Association) in particular, together with a set of 40 topics and their relevance judgements. In addition, a collection of nearly 250,000 user profiles related to the NBA collection is available. Several baseline IR experiments report the effect of using video-associated metadata on retrieval effectiveness. The results surprisingly show that searching the videos titles only performs significantly better than searching additional metadata text fields of the videos such as the tags or the description