This paper introduces DB 2 , a risk-aware behavioural identity framework that derives device identity from CPU–RTC timing deviation and Performance Monitoring Unit (PMU) microarchitectural events, without relying on GPUs, radios, sensors, or dedicated hardware. The method captures oscillator-coupled timing variation and execution behaviour through a structured signal-processing pipeline, producing device-specific behavioural signatures that remain distinguishable across reboots, temperature variation, and core transitions. DB 2 structures identity assurance into three layers: closed-set identification, calibrated open-set rejection, and stability-aware risk scoring. Evaluation under a strict three-way split with reboot separation for training, calibration, and unseen testing yields a macro-F 1 of 0.957 on unseen reboots. The open-set layer rejects previously unseen devices with a mean true-positive rate of 0.990 at a calibrated event-level false-reject rate of approximately 0.08 under strict leave-one-device-out validation, with operating-point selection performed exclusively on the calibration split. A Dynamic-Aware Identification and Risk (DAIR) mechanism decomposes behavioural stability across temperature, reboot, and core factors to provide interpretable posture monitoring for enrolled devices. Under identity-claim manipulation via spoofing, Sybil, and relabelling scenarios involving cloning, targeted identities exhibit reduced identification consistency and elevated risk, while non-targeted devices remain stable under identical calibration settings. These results show that behavioural fingerprints can be derived from standard CPU, RTC, and PMU-accessible resources on edge devices, enabling device-identity and behavioural-assurance monitoring in IoT and edge environments without specialised hardware.

The proposed functional data service framework provides a way of making heterogeneous data sources more accessible to applications and manageable to developers by integrating a service-oriented architecture, a functional data model, the semantic Web, and other related technologies. With the supports of the proposed mechanism, different data sources with their supporting programming technologies are annotated with semantics to facilitate data advertisement and discovery. The data and their relationships in the proposed framework are modelled as a number of functions to form functional data models and these models are realised as web services, so the interfaces for accessing these data can be published. Semantic Web services and ontologies are used to describe the characteristics and facets of data sources. This paper describes the structure of the proposed framework and its advantages in integrating heterogeneous data sources. The paper includes an example to illustrate the main characteristics of the proposed framework.

We present an overview of ODDI an Ontology Driven Data Integration system based on Formal concept analysis and instance comparison. Data Integration systems are used to integrate heterogeneous data sources in a single view. Following the Global-as-View approach the data is retrieved through a common conceptualization, that in our system is modeled as an ontology. This paper focuses on the problem of matching and mapping of elements between the common ontology and the data sources. The problem of query translation is also mentioned for sake of completeness, but it will be treated in detail in a future paper. Recent works on Business Intelligence do highlight the need of trustable and sound data access systems. We propose a system based on FCA to generate the mapping to the common representation and the relations between the heterogeneous data sources.

We introduce an innovative approach to wrapping semi-structured web pages in order to generate structured data. Unlike other work in this area that is based on physically specifying the location of information, our approach is based on human design psychology that captures more stable features in web pages, which we believe renders a more robust result in coping with changes in the web pages. In this paper, we focus on the product advertisement domain, and a set of design psychology principles for product advertisement is presented and used to design the wrapping rules. A case study in the mobile phone advertisement domain is used to evaluate this approach.

Knowledge fusion refers to the process of locating and extracting knowledge from multiple, heterogeneous on-line sources, and transforming it so that the union of the knowledge can be applied in problem-solving. The KRAFT project has defined a generic agent-based architecture to support fusion of knowledge in the form of constraints expressed against an object data model. KRAFT employs three kinds of agent: facilitators locate appropriate on-line sources of knowledge; wrappers transform heterogeneous knowledge to a homogeneous constraint interchange format; mediators fuse the constraints together with associated data to form a dynamically-composed constraint satisfaction problem, which is then passed to an existing constraint solver engine to compute solutions. The paper presents the KRAFT architecture and the three kinds of agent, and includes a description of a demonstration KRAFT application in the domain of telecommunications service provision.

Ontology has been investigated in the context of knowledge sharing among heterogeneous and disparate database and knowledge base systems. Our recent study and experiments suggest that ontology also have a great potential for legacy software understanding and re-engineering. In this paper we consider an ontology to be composed of four elements: classes, relations, functions and instances. We show these four elements forming an ontology for a legacy system can be extracted from the code of the concerned system using the existing software re-engineering tools. We then present our vision how the obtained ontology can be applied to understanding and eventually better re-engineering the legacy systems.

Multi Agent Systems (MAS) have been experimented in building a variety of applications. Several agent-building tools are available to assist with the tasks of building agent-based systems. However the issues on how to visualize and monitor the behaviors, and to analyze the performance of in-service agents have largely not been addressed. This paper describes the system we have developed for visualizing, monitoring and analyzing MAS activities both on-line and off-line. Our system uses an agent-based infrastructure in dealing with various service requirements from agent-based application systems concerning visualization monitoring and analysis. There are three types of agents: 1) an information management agent for collecting and storing data from MAS, and for making service agreements with MAS, 2) a visualization agent for constructing and presenting various views and 3) analysis agent for analyzing MAS activities and for making recommendations with a view to optimize the in-service agent performance.