JUpdate: A JSON Update Language

Although JSON documents are being used in several emerging applications (e.g., Big Data applications, IoT, mobile computing, smart cities, and online social networks), there is no consensual or standard language for updating JSON documents (i.e., creating, deleting or changing such documents, where changing means inserting, deleting, replacing, copying, moving, etc., portions of data in such documents). To fill this gap, we propose in this paper an SQL-like language, named JUpdate, for updating JSON documents. JUpdate is based on a set of six primitive update operations, which is proven complete and minimal, and it provides a set of fourteen user-friendly high-level operations with a well-founded semantics defined on the basis of the primitive update operations

The relationship between local structure and photo-Fenton catalytic ability of glasses and glass-ceramics prepared from Japanese slag

Local structure and the photo-Fenton reactivity of iron-containing glasses and glass-ceramics prepared from Japanese domestic waste slag were investigated. The largest rate constant (k) of (2.8 ± 0.08) × 10−2 min−1 was recorded for the methylene blue degradation test by using H2O2 with a heat-treated ‘model slag’. The 57Fe Mössbauer spectrum was composed of a paramagnetic doublet with isomer shift of 0.18 ± 0.01 mm s−1 attributed to distorted FeIIIO4 tetrahedra. These results indicate that the paramagnetic Fe3+ provided strong photo-Fenton catalytic ability, and that waste slag can thus be recycled as an effective visible-light activated photocatalyst

Highway increases concentrations of toxic metals in giant panda habitat

The Qinling panda subspecies (Ailuropoda melanoleuca qinlingensis) is highly endangered with fewer than 350 individuals inhabiting the Qinling Mountains. Previous studies have indicated that giant pandas are exposed to heavy metals, and a possible source is vehicle emission. The concentrations of Cu, Zn, Mn, Pb, Cr, Ni, Cd, Hg, and As in soil samples collected from sites along a major highway bisecting the panda's habitat were analyzed to investigate whether the highway was an important source of metal contamination. There were 11 sites along a 30-km stretch of the 108th National Highway, and at each site, soil samples were taken at four distances from the highway (0, 50, 100, and 300 m) and at three soil depths (0, 5, 10 cm). Concentrations of all metals except As exceeded background levels, and concentrations of Cu, Zn, Mn, Pb, and Cd decreased significantly with increasing distance from the highway. Geo-accumulation index indicated that topsoil next to the highway was moderately contaminated with Pb and Zn, whereas topsoil up to 300 m away from the highway was extremely contaminated with Cd. The potential ecological risk index demonstrated that this area was in a high degree of ecological hazards, which were also due to serious Cd contamination. And, the hazard quotient indicated that Cd, Pb, and Mn especially Cd could pose the health risk to giant pandas. Multivariate analyses demonstrated that the highway was the main source of Cd, Pb, and Zn and also put some influence on Mn. The study has confirmed that traffic does contaminate roadside soils and poses a potential threat to the health of pandas. This should not be ignored when the conservation and management of pandas is considered

A disciplined approach to temporal evolution and versioning support in JSON data stores

The JSON Schema language lacks explicit support for defining time-varying schemas of JSON documents. Moreover, existing JSON NoSQL databases (e.g., MongoDB, CouchDB) do not provide any support for managing temporal data. Hence, administrators of JSON NoSQL databases have to use ad hoc techniques in order to specify JSON schema for time-varying instances. In this chapter, the authors propose a disciplined approach, named Temporal JSON Schema (tauJSchema), for the temporal management of JSON documents. tauJSchema allows creating a temporal JSON schema from (1) a conventional JSON schema, (2) a set of temporal logical characteristics, for specifying which components of a JSON document can vary over time, and (3) a set of temporal physical characteristics, for specifying how the time-varying aspects are represented in the document. By using such characteristics to describe temporal aspects of JSON data, tauJSchema guarantees logical and physical data independence and provides a low-impact solution since it requires neither updates to existing JSON documents nor extensions to related JSON technologies

Implicit JSON Schema Versioning Driven by Big Data Evolution in the \u3c4JSchema Framework

In JSON-based NoSQL data stores, Big Data instance documents and their JSON schemas must evolve over time to reflect changes in the real world. When a JSON instance document, valid with respect to a JSON schema, is updated giving rise to a new document no longer valid with respect to the schema, the update is usually rejected also resulting in user frustration. In such a case, the JSON schema has to be explicitly changed by an administrator in order to become compliant with the new Big Data format before the update can be effected by the user. The different approach we propose in this work is to privilege the user actions and accept in a transparent way any update he/she wants to apply to the instance documents: violation of the validity of an updated instance document with respect to its JSON schema is automatically detected and schema changes necessary to produce a new schema version compliant with the new Big Data format are automatically applied by the system, producing a new JSON schema version. Hence, in this work, we deal with implicit JSON schema versioning driven by updates to JSON-based Big Data instance documents. Our proposed solution consists in an extension of the \u3c4JSchema (Temporal JSON Schema) framework we previously introduced to create and validate temporal JSON documents and to allow classical temporal JSON schema versioning, to also support implicit JSON schema versioning

Managing Temporal and Versioning Aspects of JSON-based Big Data via the tauJSchema Framework

Several modern applications (e.g., Internet of Things, online social networks), which exploit big data, require a complete history of all changes performed on these data and their schemas (or structures). However, although schema versioning has long been advocated to be the best solution for this issue, currently there are no available technical supports, provided by existing big data management systems (especially NoSQL DBMSs), for handling temporal evolution and versioning aspects of big data. In [14], for a disciplined and systematic approach to the temporal management of JSON-based big data in NoSQL databases, we have proposed the use of a framework, named sJSchema (temporal JSON Schema). It allows defining and validating temporal JSON documents that obey to a temporal JSON schema. A sJSchema schema is composed of a conventional (i.e., non-temporal) JSON schema annotated with a set of temporal logical and temporal physical characteristics. Moreover, since these two components could evolve over time to respond to new applications\u2019 requirements, we have extended sJSchema, in [17], to support versioning of conventional JSON schemas. In this work, we complete the figure by extending our framework to also support versioning of temporal logical and physical characteristics. Indeed, we propose a technique for temporal characteristics versioning, and provide a complete set of low-level change operations for the maintenance of these characteristics; for each operation, we define its arguments and its operational semantics. Thus, with this extension, sJSchema will provide a full support of temporal versioning of JSON-based big data at both instance and schema levels