時序性資料是常見且龐大的資料型態，無時無刻都會有新的資料生成且被記錄。社群網路是近年因網路快速發展所興起的線上虛擬社群，也因這種發展狀況，社群網路已經成為我們生活幾乎不可或缺的一部分，人與人之間的網絡關係也成為另一項重點研究項目，在社群內的各個使用者之間的連結與互動將會形成一個龐大而複雜的社群網路，並將記錄下在社群中所有用戶的的活動狀態、發言記錄、使用者之間的互動狀況及活動發生的時間，然而對於這些龐大且雜亂的社群網路的時間序列資料集合，要如何有效且迅速的篩選並處理這些資料將是本次要探討的重點。本次研究所提出的概念是結合數據分析與社群網路資料集，利用大量的時序性資料在經過篩選去除多餘資訊之後，經過標記起始時間點及結束時間點後，再利用PrefixSpan演算法去找出頻繁出現的使用者時序性序列資訊，再將其輸出結果加以記錄來實現。

Time Sequence is a common and enormous data type. Every second will generate a new set of data sequences then be recorded. Social network is a virtual online community that has developed in the last decades. Since this kind of development status, social network has become a part of our daily life. The connection between people has become another important research project. As the connection between people, the interaction and connectivity between users will form a humongous and complicated social network. This will record every user in this network’s activity status, speech record, interactive between users and record interactive time. Facing this kind of enormous and messy social network sequence datasets. How can we filter and process these datasets efficiently? We proposed a concept that uses a huge amount of time sequence data after filter them without extra information. Marked the starting-time point and end-time point. Then we used PrefixSpan algorithm to find the frequency of the user’s time sequence data, then recorded them for further usage.

中文摘要 I
英文摘要  II
圖目錄： V
表目錄： VI
第一章. 緒論 1
1.1. 引言 1
1.2. 論文架構 2
第二章. 文獻探討 3
2.1. 資料探勘 3
2.2. 時間模式 6
2.3. 社群網路 8
第三章. 系統架構 9
第四章. 實驗設計與結果 18
4.1. 實驗資料設定 18
4.2. 實驗結果 20
第五章. 結論 22
參考文獻 23
附錄 英文論文 28

圖目錄：
Figure 1. Allen’s 13 Temporal Pattern 7
Figure 2. System architectures 10
Figure 3. Program Describe 14
Figure 4. Algorithm of Pre-Processing 15
Figure 5. Algorithm of Add Time Tag 16
Figure 6. Algorithm of PrefixSpan 17
Figure 7. User Data After Filter 19
Figure 8. Pre-PrefixSpan result 20
Figure 9. Result After PrefixSpan 21
Figure 10. Final Result of Frequent User List 21

表目錄：
Table 1. Example of PrefixSpan 5
Table 2. Program Describe 11
Table 3. User sample Datasets 19

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