Research on Evaluation of Green Smart Building Based on Improved AHP-FCE Method

With the accelerated pace of urbanization, green buildings and green smart buildings gradually come into people’s vision and are highly valued by all sectors of society on the premise of meeting sustainable development strategy. Firstly, this paper selects 7 first-level index factors and 20 second-level index factors to establish the green smart building evaluation system. Secondly, this paper uses the analytic hierarchy process-fuzzy comprehensive evaluation (AHP-FCE) method to determine the weight of each secondary index. Finally, the feasibility of the evaluation system is verified by case analysis, and some suggestions on green smart building are put forward.

Solving the Fragment Complexity of Official, Social, and Sensorial Urban Data

Cities in the big data era hold the massive urban data to create valuable information and digitally enhanced services. Sources of urban data are generally categorized as one of the three types: official, social, and sensorial, which are from the government and enterprises, social networks of citizens, and the sensor network. These types typically differ significantly from each other but are consolidated together for the smart urban services. Based on the sophisticated consolidation approaches, we argue that a new challenge, fragment complexity that represents a well-integrated data has appropriate but fragmentary schema and difficult to be queried, is ignored in the state-of-art urban data management. Comparing with predefined and rigid schema, fragmentary schema means a dataset contains millions of attributes but nonorthogonally distributed among tables, and of course, values of these attributes are even massive. As far as a query is concerned, locating where these attributes are being stored is the first encountered problem, while traditional value-based query optimization has no contributions. To address this problem, we propose an index on massive attributes as an attributes-oriented optimization, namely, attribute index. Attribute index is a secondary index for locating files in which the target attributes are stored. It contains three parts: ATree for searching keys, DTree for locating keys among files, and ADLinks as a mapping table between ATree and DTree. In this paper, the index architecture, logical structure and algorithms, the implementation details, the creation process, the integration to the existing key-value store, and the urban application scenario are described. Experiments show that, in comparison with B + -Tree, LSM-Tree, and AVL-Tree, the query time of ATree is 1.1x, 1.5x, and 1.2x faster, respectively. Finally, we integrate our proposition with HBase, namely, UrbanBase, whose query performance is 1.3x faster than the original HBase.

The Social Integration of American Cities: Network Measures of Connectedness Based on Everyday Mobility Across Neighborhoods

The social integration of a city depends on the extent to which people from different neighborhoods have the opportunity to interact with one another, but most prior work has not developed formal ways of conceptualizing and measuring this kind of connectedness. In this article, we develop original, network-based measures of what we call “structural connectedness” based on the everyday travel of people across neighborhoods. Our principal index captures the extent to which residents in each neighborhood of a city travel to all other neighborhoods in equal proportion. Our secondary index captures the extent to which travels within a city are concentrated in a handful of receiving neighborhoods. We illustrate the value of our indices for the 50 largest American cities based on hundreds of millions of geotagged tweets over 18 months. We uncover important features of major American cities, including the extent to which their connectedness depends on a few neighborhood hubs, and the fact that in several cities, contact between some neighborhoods is all but nonexistent. We also show that cities with greater population densities, more cosmopolitanism, and less racial segregation have higher levels of structural connectedness. Our indices can be applied to data at any spatial scale, and our measures pave the way for more powerful and precise analyses of structural connectedness and its effects across a broad array of social phenomena.

THBase: A Coprocessor-Based Scheme for Big Trajectory Data Management

The rapid development of distributed technology has made it possible to store and query massive trajectory data. As a result, a variety of schemes for big trajectory data management have been proposed. However, the factor of data transmission is not considered in most of these, resulting in a certain impact on query efficiency. In view of that, we present THBase, a coprocessor-based scheme for big trajectory data management in HBase. THBase introduces a segment-based data model and a moving-object-based partition model to solve massive trajectory data storage, and exploits a hybrid local secondary index structure based on Observer coprocessor to accelerate spatiotemporal queries. Furthermore, it adopts certain maintenance strategies to ensure the colocation of relevant data. Based on these, THBase designs node-locality-based parallel query algorithms by Endpoint coprocessor to reduce the overhead caused by data transmission, thus ensuring efficient query performance. Experiments on datasets of ship trajectory show that our schemes can significantly outperform other schemes.

A Method of HBase Multi-Conditional Query for Ubiquitous Sensing Applications

Big data gathered from real systems, such as public infrastructure, healthcare, smart homes, industries, and so on, by sensor networks contain enormous value, and need to be mined deeply, which depends on a data storing and retrieving service. HBase is playing an increasingly important part in the big data environment since it provides a flexible pattern for storing extremely large amounts of unstructured data. Despite the fast-speed reading by RowKey, HBase does not natively support multi-conditional query, which is a common demand and operation in relational databases, especially for data analysis of ubiquitous sensing applications. In this paper, we introduce a method to construct a linear index by employing a Hilbert space-filling curve. As a RowKey generating schema, the proposed method maps multiple index-columns into a one-dimensional encoded sequence, and then constructs a new RowKey. We also provide a R-tree-based optimization to reduce the computational cost of encoding query conditions. Without using a secondary index mode, experimental results indicate that the proposed method has better performance in multi-conditional queries.