## Khamis Abdul-Latif Khamis* ** , Huazhu Song* and Xian Zhong*## |

Class | Verbs | Remarks |
---|---|---|

Vehicle (CAR) | Drive, Arrive, Depart, Pass, Park, Turn, Retrace, etc. | Motion |

Trees, Grass Plot, Garden Plot, Traffic sign, Traffic Light, Fancy | Size (Big, small) Scattered, position (Front, back, left, right), color (red, blue, white green), etc. | Non-moving |

The problem may rise when the annotation information fails to provide a real interpretation for a single and or for a partitioned node, such as the distance and magnitude of a particular node or its neighboring nodes. In this case, we propose spider-net node relation (SPnR) method, which uses semantic and vector approach to compute the distance and magnitude of the nearest nodes partitions. This approach has been introduced to enhance our semantic annotations to some extent, and that can be a useful tool during the execution of semantic queries in the retrieval and storing of media data.

The identification and the type of relations for partitioned DCs object can be classified with aspect of direction and magnitude from the neighboring nodes which could indicate the actual position and direction of the required node.

While discussing the state of partitioned object of the DCs data, proper verbs for annotation object were selected to imply a semantic labeling concept, in order for computer to easily determine relational attributes associated with the DCs data. After that we can use it to process semantic illustration based on the position of partitioned object within the required scale (direction and even magnitude of a moving or static object). However, to form all related concepts into a uniform structure, the classification of media object should be obtained first and then formulated into several regions before putting some annotation on a temporary scale.

Our main concern is to provide a proper methodology for discovering partitioned nodes of media object that has been formalized with semantic annotation. Therefore, the SPnR method uses vector methods to compute and evaluate the magnitudes and the distance relation of the partitioned object for the nearest object nodes. The node relations given in Fig. 4 is intended to find the scalable relation for the in-spot object (in-term of direction or magnitude). This technique helps us to observe many distance relations and magnitude for the most related and nearest nodes within the media object.

During the segmentation process, each segment is assigned to its closest partition and each partitioned object is already labeled (annotated). Therefore, the SPnR method can now be introduced to compute the distance and magnitude between one partitioned object and the closest object. We implement the SPnR method to measure the distance and compute the magnitude between two dimensional vectors for the nearest node. Fig. 4(b) provides an example of the SPnR approach.

If [TeX:] $$V_{1}, V_{2}, \ldots, V_{n}$$ represent the distance values between two points with Cartesian coordinates [TeX:] $$A_{1}\left(x_{1}, y_{1}\right)$$ and [TeX:] $$A_{2}\left(x_{2}, y_{2}\right)$$ then the following formula is applied as Eq. (3).

If we take [TeX:] $$A_{1}$$ as our focus point of relation, which is corresponding to point [TeX:] $$A_{2}, \ldots, A_{n}$$ with vector consideration, then we need to determine both magnitude and a direction of partitioned object [TeX:] $$V_{1}, V_{2},...,V_{n}.$$ To calculate the relational direction of each individual partitioned object, we must consider the relational vector V in 2-spaces that can specify the components in the A(x) and A(y) directions respectively.

Here “A” represents a set of the relation between a class/concepts for a given set of property A such that [TeX:] $$\left\{A_{1}, A_{2}, \ldots, A_{n}\right\}$$ and for a particular relational property, [TeX:] $$\mathrm{A}_{1}$$ is a focused point of scalable relation. Therefore, a given relational vector with the 2 dimensional magnitude [TeX:] $$V_{n}=\left(A_{n-1}\left(x_{n-1}, y_{n-1}\right), A_{n}\left(x_{n}, y_{n}\right)\right)$$ is calculated according to Equation (4).

If [TeX:] $$A_{l}$$ represents a type of partitioned object applied for a particular subject that holds RDF data [TeX:] $$\Phi_{R D F-}p$$ Subject and[TeX:] $$A_{2}$$ represents an object relation of partition data [TeX:] $$\Phi_{R D F-p} Object,$$ to calculate the distance vector relation based on the RDF graph model shown in as Eq. (5), we deduce the various vector entities to match up with RDF graph term as Eq. (5).

While [TeX:] $$\left(x_{n}-x_{n-1}\right) \text { and }\left(y_{n}-y_{n-1}\right)$$ represent a focus point of relational partition object. Therefore, the twodimensional points could be used to represent nodes and edges of relational partitioned data set that identifies the subject and object of RDF graph pattern. Generally, the distance relational vector between two nodes could then be represented as Eq. (6).

We can then compute the semantic direction of each relational object using the same vector theory shown in Eq. (7), whereby the direction of vector [TeX:] $$V_{n}$$ denoted as [TeX:] $$\theta$$ is the angle that the vector [TeX:] $$V_{n}$$ makes in the counterclockwise direction with the positive x-axis.

where [TeX:] $$A_{x}$$ is the horizontal change and [TeX:] $$A_{y}$$ is the vertical change in the partitioned object, and if we consider the initial and terminal point of distance, then [TeX:] $$A_{n}\left(x_{n}, y_{n}\right)$$ is the initial point, [TeX:] $$A_{n-1}\left(x_{n-1}, y_{x-1}\right)$$ is the terminal point of a chosen relation, and [TeX:] $$\theta$$ is got as Equation (8).

Since many SPARQL query processing are more efficient to the retrieval or access of the RDF data, our intention is to enhance SPARQL query mechanism and techniques that could retrieve the DCs data particularly for partitioned nodes with RDF serialization format. For successfully achieve our study goal, the SPARQL query processing seems to have many capabilities of accessing and retrieving the DCs data more accurately, and it can also regenerate more knowledge from the existing RDF storage. Therefore, our DCs data can be stored in a fashionable and more convenient way to the retrieval systems that can energize the query processing to perform both serialization and parallel processing over parts of the data (e.g. the portioned object), and provide a faster response to the end user. Hence, a basic graph pattern (BGP) methodology is used to improve the query processing of DCs data. This gives us an opportunity to understand the correct mode of the DCs data.

The BGP can be implemented at structured level meaning that, the first step is to reengineering the query processor to work with the DCs ontology model structure. This means that the written SPARQL query must be built on and conform to the standard format of SPARQL query, and be syntactically correct and approved by query processor. A SELECT SPARQL query is expressed, and its structure resembles much with the SQL SELECT query language. Consider the following query below:

where [TeX:] $$\mu$$ is the URL of an RDF data graph G, gp is a SPARQL graph pattern and [TeX:] $$\vec{B}$$ is a RDF tuple of variables appearing in P. The following query is formulated in SPARQL structure which states that, the query must return everything from the DCs storage model.

[TeX:] $$\begin{aligned} &\boldsymbol{S} \boldsymbol{E} \boldsymbol{L} \boldsymbol{E} \boldsymbol{C} \boldsymbol{T} \boldsymbol{*}\\ &\text {WHERE }\left\{? \boldsymbol{\Phi}_{R D F-t p} \text { Subject }<? \boldsymbol{\Phi}_{R D F-t p} \text { Predicate }>? \boldsymbol{\Phi}_{R D F-t p} \text { Object; }\right\} \end{aligned}$$

This query evaluates the data at the structured level, where each single data in a triple statement of ([TeX:] $$\Phi_{R D F-t p} S u b j e c t, \quad \Phi_{R D F-t p} \text {Predicate}, \quad \Phi_{R D F-t p} O b j e c t$$) is evaluated, which means that each triple will be interpreted individually and return the results for all matching elements in the RDF/OWL data store. Any mistake existed in structure or syntax may result in unnecessary errors. Therefore, querying at this level means that the DCs ontology model can be interpreted as a set of triples including those elements which have been given special semantics in RDF Schema.

Basically, the SPARQL supports the access of DCs data with serialized format that supports BGP and sub-graph matching. The BGP theories can match against whatever is being queried and the results of matching pattern can be fed into the semantic of SPARQL.

**Definition 1.** The BGP of SPARQL query [TeX:] $$\left(\Phi_{\mathrm{RDF}-\mathrm{bgp}}\right)$$ is a set of triple-patterns corresponding to the RDF triple [TeX:] $$\Phi_{\mathrm{RDF}-\mathrm{tp}},$$ in which zero or more convenient DCs variables [TeX:] $$V_{\mathrm{var}}$$ might appear. A solution to a SPARQL for the [TeX:] $$\Phi_{\mathrm{RDF}-\mathrm{bgp}}$$ of DCs object is a mapping μ from the query variables in the RDF terms such that the substitution of DCs variables in the [TeX:] $$\Phi_{\mathrm{RDF}-\mathrm{bgp}}$$ would yield a subgraph of [TeX:] $$\Phi_{\mathrm{RDF}-\mathrm{g}}.$$ If [TeX:] $$\Phi_{\mathrm{RDF}-\mathrm{g}}$$ is a complex graph pattern, then [TeX:] $$\left[\Phi_{\mathrm{RDF}-\mathrm{bgp}}\right] \begin{array}{l} \Phi_{\mathrm{RDF}-\mathrm{DS}} \\ \Phi_{\mathrm{RDF}-\mathrm{g}} \end{array}$$ can be defined as given in the Table 2.

According to the formal definitions given in Table 2, it is specified that, the SPARQL of [TeX:] $$\Phi_{\mathrm{RDF}-\mathrm{g}}$$ can be recursively deduced by defining the [TeX:] $$\Phi_{\mathrm{RDF}-\mathrm{g}}$$ as follows.

Table 2.

Definition | Operation |
---|---|

Graph pattern [TeX:] $$\Phi_{\mathrm{RDF}-\mathrm{bgp}}$$ | Evaluation of [TeX:] $$\left[\Phi_{\mathrm{RDF}-\mathrm{bgp}}\right]_{\Phi_{\mathrm{RDF}-\mathrm{g}}}^{\Phi_{\mathrm{RDF}-\mathrm{DS}}}$$ |

[TeX:] $$\Phi_{\mathrm{RDF}-\mathrm{bgp}} 1$$ AND [TeX:] $$\Phi_{\mathrm{RDF}-\mathrm{bgp}} 2$$ | [TeX:] $$\left[\Phi_{\mathrm{RDF}-\mathrm{bgp}} 1\right]_{\Phi_{\mathrm{RDF}-\mathrm{g}}}^{\Phi_{\mathrm{RDF}-\mathrm{DS}}}\cap \ \left[\Phi_{\mathrm{RDF}-\mathrm{bgp}} 2\right]_{\Phi_{\mathrm{RDF}-\mathrm{g}}}^{\Phi_{\mathrm{RDF}-\mathrm{DS}}}$$ |

[TeX:] $$\Phi_{\mathrm{RDF}-\mathrm{bgp}} 1$$ OPT [TeX:] $$\Phi_{\mathrm{RDF}-\mathrm{bgp}} 2$$ | [TeX:] $$\left[\Phi_{\mathrm{RDF}-\mathrm{bgp}} 1\right]_{\Phi_{\mathrm{RDF}-\mathrm{g}}}^{\Phi_{\mathrm{RDF}-\mathrm{DS}}}$$ OPT [TeX:] $$\left[\Phi_{\mathrm{RDF}-\mathrm{bgp}} 2\right]_{\Phi_{\mathrm{RDF}-\mathrm{g}}}^{\Phi_{\mathrm{RDF}-\mathrm{DS}}}$$ |

[TeX:] $$\Phi_{\mathrm{RDF}-\mathrm{bgp}} 1$$ UNION [TeX:] $$\Phi_{\mathrm{RDF}-\mathrm{bgp}} 2$$ | [TeX:] $$\left[\Phi_{\mathrm{RDF}-\mathrm{bgp}} 1\right]_{\Phi_{\mathrm{RDF}-\mathrm{g}}}^{\Phi_{\mathrm{RDF}-\mathrm{DS}}} \ \cup\left[\Phi_{\mathrm{RDF}-\mathrm{bgp}} 2\right]_{\Phi_{\mathrm{RDF}-\mathrm{g}}}^{\Phi_{\mathrm{RDF}-\mathrm{DS}}}$$ |

[TeX:] $$\Phi_{\mathrm{RDF}-\mathrm{bgp}} 1$$ FILTER C | [TeX:] $$\left\{\mu | \mu \in\left[\Phi_{\mathrm{RDF}-\mathrm{bgp}} 1\right]_{\Phi_{\mathrm{RDF}-\mathrm{g}}}^{\Phi_{\mathrm{RDF}-\mathrm{DS}}}\right. and \ \ \mu |=C\}$$ |

[TeX:] $$\mu$$ GRAPH[TeX:] $$\Phi_{\mathrm{RDF}-\mathrm{bgp}} 1$$ | [TeX:] $$\left[\Phi_{\mathrm{RDF}-\mathrm{bgp}} 1\right]_{\operatorname{GRP}(\mu) \Phi_{\mathrm{RDF}-\mathrm{DS}}}^{\Phi_{\mathrm{RDF}-\mathrm{DS}}}$$ |

?x GRAPH [TeX:] $$\Phi_{\mathrm{RDF}-\mathrm{gp}} 1$$ | [TeX:] $$U_{v \in \text { names }}\left(\Phi_{\mathrm{RDF}-\mathrm{DS}}\right)\left(\left[\Phi_{\mathrm{RDF}-\mathrm{bgp}} 1\right]_{\mathrm{GRP}(\mu) \Phi_{\mathrm{RDF}-\mathrm{DS}}} \Phi_{\mathrm{RDF}-\mathrm{DS}}\right.\cap\{\mu ? x \rightarrow v\})$$ |

**Definition 2.** A given RDF graph pattern [TeX:] $$\Phi_{\mathrm{RDF}-\mathrm{g}}$$ either for a simple or complex DCs model is simply known as RDF triple-pattern which is made up of single triple statement <S, P, O> or it is composed with the group of triple-pattern which is logically given as Eq. (9).

This implies that: if [TeX:] $$\Phi_{\mathrm{RDF}-\mathrm{Cgp}}$$ is composed with a group of [TeX:] $$\Phi_{\mathrm{RDF}-\mathrm{g}},$$ then the query execution for the DCs object retrieval should hold all logical connections of each single graph with maximum logical operators such as (AND, UNION, FILTER GRAPH, OPT, etc.) then we can deduce the group of graph pattern according to Eq. (10):

where [TeX:] $$\Phi_{\mathrm{RDP}-\mathrm{rp}}$$ denotes a triple-pattern, C denotes a filter constraint, and “ | ” represents “OR” in logical operator where [TeX:] $$\mathrm{n} \in V_{\mathrm{uri}} \cup V_{\mathrm{var}}.$$ The evaluation of a SPARQLC graph pattern [TeX:] $$\Phi_{\mathrm{RDF}-\mathrm{g}}$$ over an RDF dataset [TeX:] $$\Phi_{\mathrm{RDF}-\mathrm{DS}}$$ having active graph [TeX:] $$\Phi_{\mathrm{RDF}-\mathrm{g}},$$ is denoted as Eq. (11).

When [TeX:] $$\Phi_{\mathrm{RDF}-\mathrm{DS}}$$ and [TeX:] $$\Phi_{\mathrm{RDF}-\mathrm{g}}$$ are clear from the context, then it can be recursively defined as Eq. (12).

where [TeX:] $$\mu\left(\Phi_{\mathrm{RDF}-\mathrm{rp}}\right)$$ is the triple obtained by replacing any of the DCs variables in [TeX:] $$\Phi_{\mathrm{RDF}-\mathrm{tp}}$$ according to mapping function [TeX:] $$\mu.$$ The decision is for [TeX:] $$\Phi_{\mathrm{RDF}-\mathrm{bgp}}$$ matching without any changes or “implementation hints”. It becomes the basis of SPARQL for the DCs object retrieval.

Query analysis for the partitioned object is needed, especially in visual surveillance of DCs data. The essential task of the BGB query methodology is to detect and track away a moving or static object by using the specified distance vector properties with a surveillance of semantic label (annotation). It also includes the inference and techniques to represent spatial relationships of partitioned object, and finally analyze and express the events in a consistent way.

In a special dynamic scene, all visible things such as different regions, moving or static objects can be regarded as entities with their own attributes. Furthermore, these entities and their attributes can be linked to a group of given concepts. From this viewpoint, the query analysis of the underlying partitioned object is based on these classified concepts and their distance vector relationships.

Querying the partitioned data in the face of dynamic or static-based DCs object, is normally based on the query conditions that were specified in the partitioned nodes during the development process of the DCs ontology model. What is needed during the query abstraction is to find out what is the right formation that classifies the partitioned object based on dynamic or static event query criteria.

This is because different segments or partitioned objects need a unified query from different tables in the database. The point is that, once we abstract events and sub-events from partition object, each individual event is converted into the instance of class objects and classified according to the internal properties it holds, where it can create one single RDF statement.

The main advantage of event query of the DCs data is to take every DCs object with respect to the human context. The retrieval effectiveness of the events can be evaluated by using the classic information retrieval measures known as Precision and Recall. Recall is the ratio of the number of relevant records retrieved from the total number of relevant records in the database (that are not retrieved), while Precision is the ratio of the number of relevant records retrieved from the total number of irrelevant and relevant records retrieved. Table 3 gives the Precision and Recall value for spatial queries. The events query obtains a higher precision and recall than the text-based approach.

Table 3.

S. No. | Input query | Precision | Recall | Remarks |
---|---|---|---|---|

1 | The car is near the road light | 0.58 | 0.62 | Text |

2 | The car is 3 meters from the road light | 0.72 | 0.7 | Event |

3 | The car is moving fast | 0.69 | 0.65 | Event |

4 | The two roads are narrow | 0.55 | 0.66 | Text |

5 | The road is marked with traffic signs | 0.15 | 0.27 | Text |

6 | Trees are too near the road | 0.35 | 0.22 | Text |

7 | The trees are green | 0.88 | 0.43 | Text |

8 | The car is moving towards north | 0.78 | 0.85 | Event |

It is observed that the ontology-based event retrieval performs the event query-based techniques better than text-based query. Fig. 5 provides an evidence-based query solution for event-based query solution vise text base query solution which depends on the Precision and Recall function.

In Fig. 5, the two lines may represent the performance of the query-based solution. It is always difficult to measure and calculate the recall for data retrieval within the database, because it is very stressful to know how many relevant records exist within the database. Therefore, in this experiment we only estimate the Recall by identifying a pool of relevant records and then determine the proportion of the pool upon query retrieval and search.

In this paper we discuss several methods and techniques that assist us on providing a constructive methodology that re-defined the DCs data using OWL/RDF technique and media segmentation approaches. We use the vector relation approach to strengthen our semantic tactics for each DCs partitioned object. The given approaches and methodology are the core factors of this study that reengineer the query solutions for the DCs ontology data.

This paper was supported in part by the National Key Technology Research and Development Program of the Ministry of Science and Technology of China under Grant 2012BAH33F03, the National Natural Science Foundation of China under Grant 61303029, and the Natural Science Foundation of Hubei Province of China under Grant 2015CFB525.

He was as PhD student with the School of Computer Science and Technology, Wuhan University of Technology, Wuhan, Hubei, China, whose main research interests are semantic and ontology. Now he is with the Department of Information Technology and Computer Science at the State University of Zanzibar, Zanzibar.

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