Proximity search on heterogeneous graphs aims to measure the proximity between two nodes on a graph w.r.t. some semantic relation for ranking. Pioneer work often tries to measure such proximity by paths connecting the two nodes. However, paths as linear sequences have limited expressiveness for the complex network connections. In this paper, we explore a more expressive DAG (directed acyclic graph) data structure for modeling the connections between two nodes. Particularly, we are interested in learning a representation for the DAGs to encode the proximity between two nodes. We face two challenges to use DAGs, including how to efficiently generate DAGs and how to effectively learn DAG embedding for proximity search. We find distance-awareness as important for proximity search and the key to solve the above challenges. Thus we develop a novel Distance-aware DAG Embedding (D2AGE) model. We evaluate D2AGE on three benchmark data sets with six semantic relations, and we show that D2AGE outperforms the state-of-the-art baselines. We release the code on https://github.com/shuaiOKshuai.

Semantic proximity search on heterogeneous graph is an important task, and is useful for many applications. It aims to measure the proximity between two nodes on a heterogeneous graph w.r.t. some given semantic relation. Prior work often tries to measure the semantic proximity by paths connecting a query object and a target object. Despite the success of such path-based approaches, they often modeled the paths in a weakly coupled manner, which overlooked the rich interactions among paths. In this paper, we introduce a novel concept of interactive paths to model the inter-dependency among multiple paths between a query object and a target object. We then propose an Interactive Paths Embedding (IPE) model, which learns low-dimensional representations for the resulting interactive-paths structures for proximity estimation. We conduct experiments on seven relations with four different types of heterogeneous graphs, and show that our model outperforms the state-of-the-art baselines.

Semantic user search is an important task on heterogeneous social networks. Its core problem is to measure the proximity between two user objects in the network w.r.t. certain semantic user relation. State-of-the-art solutions often take a path-based approach, which uses the sequences of objects connecting a query user and a target user to measure their proximity. Despite their success, we assert that path as a low-order structure is insufficient to capture the rich semantics between two users. Therefore, in this paper we introduce a new concept of subgraph-augmented path for semantic user search. Specifically, we consider sampling a set of object paths from a query user to a target user; then in each object path, we replace the linear object sequence between its every two neighboring users with their shared subgraph instances. Such subgraph-augmented paths are expected to leverage both path»s distance awareness and subgraph»s high-order structure. As it is non-trivial to model such subgraph-augmented paths, we develop a Subgraph-augmented Path Embedding (SPE) framework to accomplish the task. We evaluate our solution on six semantic user relations in three real-world public data sets, and show that it outperforms the baselines.

Many real-world networks have a rich collection of objects. The semantics of these objects allows us to capture different classes of proximities, thus enabling an important task of semantic proximity search. As the core of semantic proximity search, we have to measure the proximity on a heterogeneous graph, whose nodes are various types of objects. Most of the existing methods rely on engineering features about the graph structure between two nodes to measure their proximity. With recent development on graph embedding, we see a good chance to avoid feature engineering for semantic proximity search. There is very little work on using graph embedding for semantic proximity search. We also observe that graph embedding methods typically focus on embedding nodes, which is an "indirect'' approach to learn the proximity. Thus, we introduce a new concept of proximity embedding, which directly embeds the network structure between two possibly distant nodes. We also design our proximity embedding, so as to flexibly support both symmetric and asymmetric proximities. Based on the proximity embedding, we can easily estimate the proximity score between two nodes and enable search on the graph. We evaluate our proximity embedding method on three real-world public data sets, and show it outperforms the state-of-the-art baselines.

As Personalized PageRank has been widely leveraged for ranking on a graph, the efficient computation of Personalized PageRank Vector (PPV) becomes a prominent issue. In this paper, we propose FastPPV, an approximate PPV computation algorithm that is incremental and accuracy-aware. Our approach hinges on a novel paradigm of scheduled approximation: the computation is partitioned and scheduled for processing in an "organized" way, such that we can gradually improve our PPV estimation in an incremental manner, and quantify the accuracy of our approximation at query time. Guided by this principle, we develop an efficient hub based realization, where we adopt the metric of hub-length to partition and schedule random walk tours so that the approximation error reduces exponentially over iterations. Furthermore, as tours are segmented by hubs, the shared substructures between different tours (around the same hub) can be reused to speed up query processing both within and across iterations. Finally, we evaluate FastPPV over two real-world graphs, and show that it not only significantly outperforms two state-of-the-art baselines in both online and offline phrases, but also scale well on larger graphs. In particular, we are able to achieve near-constant time online query processing irrespective of graph size.

Most current semantic Web services (SWS) discovery approaches focus on the matchmaking of services in a specific description language while in practical application the advertised services are often heterogeneous and distributed. This paper proposes a metric space approach to resolve this problem in which all heterogeneous Web services are modeled as metric objects regardless of concrete description languages, and thereby the discovery problem can be treated as similarity search in metric space with a uniform criterion. In the matchmaking process, both the functional semantics and non-functional semantics of the Web services are integrated as selection conditions for similarity query. And two types of similarity queries: range query and an improved nearest neighbor query are combined to produce a sorted result set.

As adaptive Web applications are gaining importance in software domains nowadays, there is a need for an effective method to construct such applications. In this paper, we propose MAWA, a method for model-driven development of adaptive Web applications. MAWA considers the architecture of adaptive Web applications and the key elements of the development activity. It adopts an iterative, incremental development process, and the adaptive model, which is composed of context model and user model, is highlighted. Besides, the adaptive categories and mechanism are specified in MAWA, which are used to support the implementation of the adaptive behaviors. A code generation strategy is integrated into MAWA, and it can help us to produce the applications quickly and effectively.

An original GA (genetic algorithm) is usually used for QoS (quality of service)-based Web services selection, however, such algorithm has to do a large redundancy repeat to find a solution for the reason that it does not make use of adequate output information. Thus, the efficiency and precision of GA are reduced. Moreover, fixed fitness function can not change to fit for different composite situations and complete automatic process so GA sometimes would miss the most suitable solution. To remedy this situation, this paper proposes a novel algorithm GBAA (genetic based ant algorithm) which could put feedback information to original GA by using MMAS (max-min ant system) and it also overcome some drawbacks of MMAS such as long time need and slow convergence. The new approach could gain the benefits of both GA and MMAS. Besides GBAA DC (divide and composite) method and rank sort method are added. DC is adopted to analysis business requirement and to build process. After computing QoS attributes, a manual step is allowed to put into selection process and a rank sort method is used to distinguish profits of different solutions. The automatic process which is cooperated with manual process could help user to make more effective decisions on Web service selection.

Web services and SOA technologies are growing with a fast rate but still facing many problems due to their heterogeneous nature. This paper, based on OWL-S, presents a rich and extensible ontology framework named OWL-QSP for service compositions. In the framework, Service Type is imported to improve service abstract level, and QoS, situation, context are adopted. Since service discovery, service selection and service execution can adapt to the changing situation, QoS and situation-aware service-based systems are more dynamic and flexible so to better satisfy the users' functional and non-functional requirements. The introducing of policy permits managing WSs at a high level and facilitate reuse. It also presents SMICE, a prototype of the service composition system, and describes its main components with service composition process.

To reduce the overload of human management, recently runtime self-adaptation is emerging as an important characteristic required by most intelligent software-intensive systems. Most methods are built upon the analysis of concepts of architecture and exploit some "craft" from the perspective of qualitative analysis. However, these methods are often incapable of reasoning about the history of requested services, hence it is difficult to improve more efficiently software efficiency and predictability. Quantitative analysis based on the theory of stochastic processes would be a better option to depict the runtime environment more accurately. This paper presents a demonstration of the idea. In this paper, we employ the mathematic characteristic of Hidden Markov Model to achieve self-adaptation at runtime by means of modeling the behavior of users' requests and the runtime context. After analyzing the history of requested services and reconstructing the request sequence, the model responds to requests in a more efficient and rapid fashion.

This paper presents a framework for automated software development: Xauto, which includes four key elements: layer language, template, framework and component. Based on software system patterns, an automatic development process is supported and realized by template engineering and the mapping of layer languages. The authors expound the mapping patterns of Xauto framework in the three aspects of model, view and controller, and demonstrate them by the relevant template examples. The research on Xauto framework solidifies the mature solutions dealing with problems in certain domains and makes it reusable, and facilitates the change of software development mode from personal workshop to template engineering. This shift, therefore, will promote the automatic degree of software development and make it more efficient

Software development is considered to be a kind of collaborative activity now. In this paper, we first analyze the software development activity using activity theory, and highlight its collaborative features. We then propose an architecture of process-centered context-aware software development environment, CASDE, which fully considers the key elements of PCSDEs, especially the context element. The supportive and integrated nature of the environment is emphasized in CASDE. As illustrated in activity theory, the architecture can support the three levels of collaboration, i.e., co-ordinated, co-operative, and co-constructive level. In particular, the co-operative level is supported sufficiently by the introduction of context model and context process mechanism. Based on the architecture, the software development activity can be more collaborative and quality of software system can be improved

COTS (commercial off the shelf) component products undergo a technology refresh and renewal cycle. New versions or releases of COTS component products are brought to the market frequently. And rapid evolution of the COTS component product means the obsolescence of the COTS-based systems, especially during COTS-based systems' maintenance (in a long run). This is the fundamental problem to application developer. So the evaluation of the system's COTS component part is primary issue. This paper provides an open evaluation model to complete the task. The model bases on AHP and WSM, considering of COTS components lifecycle

This paper presents UPME, a metamodel approach to software process modeling based on UML extension. In this approach, software process modeling will take three steps: firstly, a metamodel modeling step to build metamodels for software process domain; secondly, a model instantiation step to build the model for a concrete project based on domain metamodels; and thirdly, a model compilation step to translate the model into object-oriented code skeleton for process enactment. In these steps, UML and its extension mechanisms are used, and an instantiation description language is designed to write process instantiation scripts. We built the ISPW-6 process as example, and the result demonstrated that the UML based metamodel approach makes process modeling more reusable and easier. And the popularity of UML also makes this approach more acceptable in industry than other specialized process modeling languages (PMLs).